A method for selecting items one by one from a set of items elected from a large dataset of items includes determining whether or not a density of the set is sparse. If the density is sparse, the method includes repeatedly performing an extreme item select (EIS) method to select a next one of the elected items from the set and removing the next one from the set to create a next set. If the density is not sparse, the method includes performing a next index select (NIS) method to create a linked list of the elected items and to repeatedly select a next elected item from the set.
A method to compare between a first number and a second number includes the steps of storing the first number in a first row of an associative memory array, storing a two's complement representation of the second number in a second row of the associative memory array wherein bit i of the second number is stored in a same column of the associative memory array as bit i of the first number, concurrently performing a carry save operation on a plurality of columns of the associative memory array to create a sum and a carry, predicting a value of a carry out bit without adding the sum and the carry, and indicating that the first number is smaller than the second number if the value of the carry out bit is 1.
G06F 7/48 - Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using unspecified devices
3.
IN MEMORY MATRIX MULTIPLICATION AND ITS USAGE IN NEURAL NETWORKS
A device for in memory vector-matrix multiplication includes a memory array and in-memory logic. The memory array has at least two sections and stores a multiplier matrix. The memory array also receives and stores an input multiplicand arranged in a vector such that the operands of the vector-matrix multiplication are located on a same column of the memory array. Each of the sections is one of: a volatile memory array, a non-volatile memory array, a destructive memory array and a non-destructive memory array. The in-memory logic computes an output of the vector-matrix multiplication using the stored input vector and the stored multiplier matrix. The memory array is one of the following type of memory array: RAM, DRAM, SRAM, Re-RAM, ZRAM, MRAM and Memristor.
G11C 15/00 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores
A method for an associative memory device includes performing in parallel multi-bit operations of P pairs of multi-bit operands stored in columns of a memory array, each pair is stored in a different column, each bit i of each multi-bit operands of each pair is stored in a row of a section i in the column and each operation occurs in its associated column. A system includes a non-destructive associative memory array with multiple sections, each section j includes cells arranged in rows and columns, to store a bit j from a first multi-bit number in a first row and a bit j from a second multi-bit number in a second row of a same column, and a concurrent adder to, in parallel, perform per-section operations in each section, that includes one or more Boolean operations between a plurality of bits stored in rows of the section.
G06F 7/508 - Adding; Subtracting in bit-parallel fashion, i.e. having a different digit-handling circuit for each denomination with simultaneous carry generation for, or propagation over, two or more stages using carry look-ahead circuits
G11C 15/04 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores using semiconductor elements
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
A memory device includes a plurality of memory units and a global responder (RSP) unit. Each memory unit includes a memory array of memory cells arranged in rows and columns, and an RSP unit. The memory array receives horizontal input data rotated for storage as data candidates in columns of the array. At least one of the rows is a calculation row receiving per-bit-line Boolean AND operations between bits of a marker row and bits of a row of data of the data candidates. The RSP unit includes wired-OR circuitry operative on the calculation row to generate a responder signal indicating whether there is one cell in the calculation row having a predefined value identifying a data candidate in the memory array. The global RSP unit receives multiple responder signals, one from at least two of the RSP units, and performs Boolean OR operations on the multiple responder signals.
G11C 15/00 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores
G11C 15/04 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores using semiconductor elements
A system for measuring similarity between a binary query vector and a plurality of binary candidate vectors includes a storage unit and a processor. The storage unit stores the binary query vector and the plurality of candidate vectors, and the processor performs Tanimoto calculations in terms of Hamming distances.
H03M 13/29 - Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes combining two or more codes or code structures, e.g. product codes, generalised product codes, concatenated codes, inner and outer codes
H03M 13/15 - Cyclic codes, i.e. cyclic shifts of codewords produce other codewords, e.g. codes defined by a generator polynomial, Bose-Chaudhuri-Hocquenghem [BCH] codes
G06F 18/23213 - Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
8.
Concurrent multi-bit subtraction in associative memory
A method for an associative memory device includes storing a plurality of pairs of multi-bit operands X and Y in rows of a memory array of the associative memory device, each pair in a different column of the memory array. Cells in a column are connected by a first bit-line providing a value of activated cells and a second bit-line providing an inverse value of the activated cells. The bits of X are stored in first rows and the bits of Y are stored in second rows. The method includes reading an inverse value of a bit stored in each of the second rows using the second bit-line, writing it to third rows and concurrently, on all columns, performing multi-bit add operations between a value of X, an inverse value of Y and a carry-in bit initiated to 1, providing the difference between X and Y in each of the columns.
A method for calculating a square root B having N bits of a number X having 2N bits includes iterating on bits bi of square root B starting from the most significant bit until the least significant bit of square root B. For each iteration, the method includes locating a 1 at the squared location of bit bi in a CHECK variable, determining the value of bit bi from the result of a comparison of number X with a function of all previously found bits and a previous comparison outcome, shifting all previously found bits right 1 location in a CHECK variable, and adding the determined value of bit bi into its squared location in the CHECK variable.
G06F 5/01 - Methods or arrangements for data conversion without changing the order or content of the data handled for shifting, e.g. justifying, scaling, normalising
G06F 7/537 - Reduction of the number of iteration steps or stages, e.g. using the Sweeny-Robertson-Tocher [SRT] algorithm
A system to retrieve medical X-rays includes a trained convolutional neural network (CNN), a balancing feature generator, a balancing type selector, and a K-Nearest Neighbor (KNN) classifier. The trained CNN encodes a plurality of diagnosed X-ray images into a plurality of candidate embeddings, and encodes a partially diagnosed X-ray image into a query embedding. The balancing feature generator produces a plurality of virtual candidate embeddings from the query embedding and the plurality of candidate embeddings. The balancing type selector selects a subset of the plurality of virtual candidate embeddings. The KNN classifier performs a KNN search between the query embedding and a plurality of the candidate embeddings and the subset of the plurality of virtual candidate embeddings.
A cascading search system includes an associative memory array, a similarity match processor and an exact match processor. The columns of the array store a plurality of multiportion data vectors and have a first section, for a first portion of a vector, a second section for storing a second portion of a vector and a match row. The similarity match processor performs a parallel similarity search of a similarity query in the first sections and stores a match bit indication in the match row of the column. Each match bit indication indicates if its column has a first portion which matches the similarity query. The exact match processor performs an exact search in parallel in the second section of each similarity matched column whose match bit indication indicates a match of its first section and outputs those similarity matched columns whose second portions match the exact query.
A system for training a neural-network-based floating-point-to-binary feature vector encoder preserves the locality relationships between samples in an input space over to an output space. The system includes a neural network under training and a probability distribution loss function generator. The neural network has floating-point inputs and floating-point pseudo-bipolar outputs. The generator compares an input probability distribution constructed from floating-point cosine similarities of an input space and an output probability distribution constructed from floating-point pseudo-bipolar pseudo-Hamming similarities of an output space. The system includes a proxy vector set generator to take a random sampling of vectors from training data for a proxy set, a sample vector selector to select sample vectors from the training data and a KNN vector set generator to find a set of k nearest neighbors closest to each sample vector from said proxy set for a reference set.
A method for concurrently performing multiple computations in an associative processing unit (APU) includes having data in two matrices, representing data in two portions of a memory array of the APU, creating a Tartan matrix by computing an outer product between a first bit vector indicating selected rows and a second bit vector indicating selected columns, the Tartan matrix representing data stored in a third portion of the memory array wherein all cells having a value 1 in the Tartan matrix indicate selected cells, concurrently activating all cells of the matrices and storing a result of Boolean operations therebetween in one of the two matrices, wherein a new value is obtained on cells located at a same row and a same column as the selected cells in the Tartan matrix and an original value remains on other cells.
G06F 15/80 - Architectures of general purpose stored program computers comprising an array of processing units with common control, e.g. single instruction multiple data processors
15.
CORDIC COMPUTATION OF SIN/COS USING COMBINED APPROACH IN ASSOCIATIVE MEMORY
A method for an associative memory device includes the steps of providing a look up table (LUT) with all possible solutions for N first iterations of a CORDIC algorithm, receiving a plurality of input angles, concurrently computing a location index for each angle of the plurality of angles and concurrently storing each index in a column of the associative memory device, copying a solution from the LUT in the location index to a plurality of columns associated with the index and concurrently performing M additional iterations of the CORDIC algorithm on the columns to compute a value of a trigonometric function for each angle.
G06F 7/48 - Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using unspecified devices
A system for cancer type prediction includes a trained neural network (NN), a patient feature-set extractor (PFE), and an associative feature-set searcher (FSS). The trained NN receives a patient input vector from a patient record and generates cancer type predictions. The PFE extracts a known cancer feature set from patient input vector from a patient record with a known cancer type, and an unknown cancer feature set from a patient input vector from a patient record without a known cancer type, when passed through the trained NN. The FSS stores a known cancer feature set in a first portion of a column, and metadata in a second portion of a column, and finds K nearest neighbors of the unknown cancer feature set from among the stored known cancer feature sets.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
An associative graph search system includes a KNN graph determiner to determine in advance W neighbors of each item in a dataset and to store each item and its neighbors in a KNN graph, a reduced dimension vector finder implemented on an associative processing unit (APU) to find a first number of first nearest neighbors of a query vector, the APU operating in a constant complexity irrespective of the size of the number, a result expander to find for each first nearest neighbor, W second nearest neighbors using the KNN graph thereby creating a group of neighbors, and a KNN full dimension vector re-ranker to find a final number of full dimension nearest neighbors of the full dimension query vector from the group of neighbors.
A system to dynamically calculate a root hash value from a plurality of leaf hash values includes a flat associative memory and a hash parser. The flat associative memory stores a plurality of leaf hash values. The hash parser extracts a compressed number of branch nodes from the plurality of leaf hash values, determines branch node relationships from the plurality of leaf hash values, and saves the compressed number of branch nodes, and the branch node relationships.
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
H04L 9/06 - Arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
19.
N-GRAM BASED CLASSIFICATION WITH ASSOCIATIVE PROCESSING UNIT
A system for N-gram classification in a field of interest via hyperdimensional computing includes an associative memory array and a controller. The associative memory array stores hyperdimensional vectors in rows of the array. The hyperdimensional vectors represent symbols in the field of interest and the array includes bit-line processors along portions of bit-lines of the array. The controller activates rows of the array to perform XNOR, permute, and add operations on the hyperdimensional vectors with the bit-line processors, to encode N-grams, having N symbols therein, to generate fingerprints of a portion of the field of interest from the N-grams, to store the fingerprints within the associative memory array, and to match an input sequence to one of the stored fingerprints.
G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking
G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
G11C 7/12 - Bit line control circuits, e.g. drivers, boosters, pull-up circuits, pull-down circuits, precharging circuits, equalising circuits, for bit lines
20.
N-GRAM BASED CLASSIFICATION WITH ASSOCIATIVE PROCESSING UNIT
A system for N-gram classification in a field of interest via hyperdimensional computing includes an associative memory array and a controller. The associative memory array stores hyperdimensional vectors in rows of the array. The hyperdimensional vectors represent symbols in the field of interest and the array includes bit-line processors along portions of bit-lines of the array. The controller activates rows of the array to perform XNOR, permute, and add operations on the hyperdimensional vectors with the bit-line processors, to encode N-grams, having N symbols therein, to generate fingerprints of a portion of the field of interest from the N-grams, to store the fingerprints within the associative memory array, and to match an input sequence to one of the stored fingerprints.
A system to generate true random numbers includes a RAM array, a null-read controller and a hash generator. The RAM array has memory cells and a sense amplifier. The memory cells store data therein, the cells are connected in rows to word lines and in columns to pairs of bit lines, and the sense amplifier senses a differential input signal. The null-read controller implements a null-read operation by the sense amplifier of a portion of the RAM array. The hash generator receives a null-read result from the null-read operation and outputs a partial true random number based on the null read result.
G11C 11/40 - Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements using semiconductor devices using transistors
G11C 11/406 - Management or control of the refreshing or charge-regeneration cycles
G11C 11/407 - Auxiliary circuits, e.g. for addressing, decoding, driving, writing, sensing or timing for memory cells of the field-effect type
G11C 11/4072 - Circuits for initialization, powering up or down, clearing memory or presetting
A system to generate true random numbers includes a RAM array, a null-read controller and a hash generator. The RAM array has memory cells and a sense amplifier. The memory cells store data therein, the cells are connected in rows to word lines and in columns to pairs of bit lines, and the sense amplifier senses a differential input signal. The null-read controller implements a null-read operation by the sense amplifier of a portion of the RAM array. The hash generator receives a null-read result from the null-read operation and outputs a partial true random number based on the null read result
G11C 11/412 - Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements using semiconductor devices using transistors forming cells with positive feedback, i.e. cells not needing refreshing or charge regeneration, e.g. bistable multivibrator or Schmitt trigger using field-effect transistors only
42 - Scientific, technological and industrial services, research and design
Goods & Services
Software as a Services (SaaS) software that optimizes neural search applications; Software as a Services (SaaS) software that uses artificial intelligence and machine learning to search and filter datasets; providing on-line nondownloadable software that uses artificial intelligence and machine learning to search databases of text, images, video and audio; providing on-line nondownloadable software for data analysis, data processing, data queries, data indexing, and data searching
24.
SYSTEM AND METHOD FOR IMPROVED SIMILARITY SEARCH FOR SEARCH ENGINES
A system and method for an improved similarity search for an Elasticsearch engine includes an accelerated processing unit (APU) to process a vector query for a similarity search using cosine similarity; and a plugin to said Elasticsearch engine to identify a vector query uploaded to the Elasticsearch engine by a user, to divert the vector query to the APU for processing and to return a set of results to the user for the similarity search, each result having an index and ordinal scale representing its distance from the vector query.
A system for parallel combinatorial design includes a processor, an in-memory vector processor and a storage unit. The processor includes a seed generator, a Cspan generator and a rule checker. The seed generator generates at least one seed to generate combinations of length N, defining a space of N choices of which M choices are to be selected. The Cspan generator generates at least one combination from the at least one seed and stores each combination in a separate column of the in-memory vector processor. The rule checker performs a parallel search at least in the in-memory vector processor for combinations which satisfy a rule and the storage unit receives search results of the rule checker from the in-memory vector processor.
H04L 9/00 - Arrangements for secret or secure communications; Network security protocols
G06F 7/58 - Random or pseudo-random number generators
H02H 3/05 - Emergency protective circuit arrangements for automatic disconnection directly responsive to an undesired change from normal electric working condition, with or without subsequent reconnection - Details with means for increasing reliability, e.g. redundancy arrangements
26.
SYSTEM AND METHOD FOR PARALLEL COMBINATORIAL DESIGN
A system for parallel combinatorial design includes a processor, an in-memory vector processor and a storage unit. The processor includes a seed generator, a Cspan generator and a rule checker. The seed generator generates at least one seed to generate combinations of length N, defining a space of N choices of which M choices are to be selected. The Cspan generator generates at least one combination from the at least one seed and stores each combination in a separate column of the in-memory vector processor. The rule checker performs a parallel search at least in the in-memory vector processor for combinations which satisfy a rule and the storage unit receives search results of the rule checker from the in-memory vector processor.
A protein searcher includes a pre-trained CNN, a feature extractor, a database and a KNN searcher. The pre-trained CNN, trained on a previously classified amino acid database, receives an unidentified amino acid sequence. The feature extractor extracts a feature vector of the unidentified amino acid sequence as a query feature vector. The database stores feature vectors of trained amino acid sequences and of at least one untrained amino acid sequence and stores associated classes of the trained amino acid sequences and associated tags of the at least one untrained amino acid sequence. The KNN searcher finds K feature vectors of the database which are close to the query feature vector and outputs the associated class or tag of each of the K feature vectors.
A protein searcher includes a pre-trained CNN, a feature extractor, a database and a KNN searcher. The pre-trained CNN, trained on a previously classified amino acid database, receives an unidentified amino acid sequence. The feature extractor extracts a feature vector of the unidentified amino acid sequence as a query feature vector. The database stores feature vectors of trained amino acid sequences and of at least one untrained amino acid sequence and stores associated classes of the trained amino acid sequences and associated tags of the at least one untrained amino acid sequence. The KNN searcher finds K feature vectors of the database which are close to the query feature vector and outputs the associated class or tag of each of the K feature vectors.
A storage array for computational memory cells formed as a memory/processing array provides storage of the data without using the more complicated computational memory cells for storage. The storage array may have multiple columns of the storage cells coupled to a column of the computational memory cells. The storage array may have ECC circuitry.
A system for training a neural-network-based floating-point-to-binary feature vector encoder preserves the locality relationships between samples in an input space over to an output space. The system includes a neural network under training and a probability distribution loss function generator. The neural network has floating-point inputs and floating-point pseudo-bipolar outputs. The generator compares an input probability distribution constructed from floating-point cosine similarities of an input space and an output probability distribution constructed from floating-point pseudo-bipolar pseudo-Hamming similarities of an output space. The system includes a proxy vector set generator to take a random sampling of vectors from training data for a proxy set, a sample vector selector to select sample vectors from the training data and a KNN vector set generator to find a set of k nearest neighbors closest to each sample vector from said proxy set for a reference set.
A read register is provided that captures and stores the read result on a read bit line connected to a set of computational memory cells. The read register may be implemented in the set of computational memory cell to enable the logical XOR, logical AND, and/or logical OR accumulation of read results in the read register. The set of computational memory cells with the read register provides a mechanism for performing complex logical functions across multiple computational memory cells connected to the same read bit line.
A system including a secure, in-memory unit implemented on an associative processing unit (APU), for creating encrypted vectors. The in-memory unit includes a data store and an encryptor. The data store stores data and the encryptor encrypts the data into an encrypted vector. Optionally, the unit includes a neural proxy hash encoder that encodes the data into an encoded vector, and, in this embodiment, the encryptor encrypts the encoded vector into an encrypted encoded vector. The neural proxy hash encoder includes a trained neural network which includes a plurality of layers that encode the data into feature sets. The trained neural network encodes image files, audio files, or large data sets. The APU is implemented on SRAM, non-volatile, or non-destructive memory.
G06F 21/79 - Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure storage of data in semiconductor storage media, e.g. directly-addressable memories
G06F 21/72 - Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information in cryptographic circuits
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06N 3/04 - Architecture, e.g. interconnection topology
A method to determine an extreme value of a plurality of data candidates includes storing each data candidate of a plurality of data candidates in a separate column of an associative memory, initializing a row of marker bits by setting each marker bit to a value of 1, computing a subsequent row of marker bits by performing in parallel a Boolean AND operation between a previous row of marker bits and a row of bits of the data candidates, starting with the row of most significant bits of the data candidates, performing a Boolean OR operation between the marker bits in the subsequent row of marker bits to generate a subsequent RSP value, identifying the extreme value from among the plurality of data candidates when there is only one marker bit having a value of 1 in the subsequent row of marker bits coinciding with when said subsequent RSP value is a 1, and if the identifying is false, repeating the computing on a row of next most significant bits, performing and identifying until the identifying is true.
G11C 15/00 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores
G11C 15/04 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores using semiconductor elements
A system for detecting changes between two temporally different images includes an image divider, a Convolutional Neural Network (CNN) feature encoder, an image alignment system, a feature comparator, a CNN feature decoder and segmenter, and a block combiner. The image divider divides a first and second image into a plurality of image blocks. CNN feature encoder encodes the image blocks from the first and second image into first and second feature sets respectively. The image alignment system aligns the first and second image by searching for matching anchor vectors in the first and second feature sets using a similarity search. The feature comparator produces change feature sets from the first and second feature sets of the aligned image blocks, and the CNN feature decoder and segmenter creates segmented change image blocks from the change feature sets. The block combiner combines segmented change image blocks into a segmented change image.
A system for measuring similarity between a binary query vector and a plurality of binary candidate vectors includes a storage unit and a processor. The storage unit stores the binary query vector and the plurality of candidate vectors, and the processor performs Tanimoto calculations in terms of Hamming distances. The processor includes a Tanimoto to Hamming threshold converter, a Hamming measurer, and a Hamming comparator. The Tanimoto to Hamming threshold converter converts a Tanimoto threshold into a Hamming threshold. The Hamming measurer measures the Hamming distances between the candidate vectors and the query vector. The Hamming comparator selects candidate vectors whose Hamming distance from the query vector is less than or equal to the Hamming threshold.
H03M 13/29 - Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes combining two or more codes or code structures, e.g. product codes, generalised product codes, concatenated codes, inner and outer codes
H03M 13/15 - Cyclic codes, i.e. cyclic shifts of codewords produce other codewords, e.g. codes defined by a generator polynomial, Bose-Chaudhuri-Hocquenghem [BCH] codes
G06F 18/23213 - Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
A system for finding similar molecules to a query molecule includes a GCN, a PFS vector extractor, a compensated vector comparator (CVC) and a candidate vector selector. The GCN has been trained to output a molecular property vector from an input query or input candidate molecular vectors, respectively, The GCN transforms query atomic feature set (AFS) vectors and candidate AFS vectors into query property feature set (PFS) embedding vectors and candidate PFS embedding vectors. The PFS vector extractor extracts query PFS embedding vectors and candidate PFS embedding vectors from hidden layers of the trained GCN. The compensated vector comparator (CVC) calculates a compensated similarity metric (CSM) for at least one pair of query PFS embedding vector and one candidate PFS embedding vector. The candidate vector selector selects only such candidate molecular vectors.
A method for binary division includes the steps of having a current remainder provided as a sum bit-vector and a carry bit-vector, performing a carry save add operation between the sum bit-vector and the carry bit-vector and a two's complement representation of a denominator to produce a temporary sum and a temporary carry, predicting a sign bit of a full total of the temporary sum and the temporary carry and updating the remainder with the temporary sum and the temporary carry and incrementing a quotient if the sign bit is 0.
G06F 7/48 - Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using unspecified devices
38.
Write data processing circuits and methods associated with computational memory cells
A write data processing apparatus and method associated with computational memory cells formed as a memory/processing array provides the ability to shift data between adjacent bit lines in each section of the memory/processing array or the same relative bit lines in adjacent sections of the memory/processing array. The memory/processing array has one or more sections and each section has its own unique set of “n” bit lines.
H03K 19/0944 - Logic circuits, i.e. having at least two inputs acting on one output; Inverting circuits using specified components using semiconductor devices using field-effect transistors using MOSFET
39.
Computational memory cell and processing array device using the memory cells for XOR and XNOR computations
A memory cell and processing array that has a plurality of memory are capable of performing logic functions, including an exclusive OR (XOR) or an exclusive NOR (XNOR) logic function. The memory cell may have a read port in which the digital data stored in the storage cell of the memory cell is isolated from the read bit line.
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
G11C 7/18 - Bit line organisation; Bit line lay-out
G11C 15/04 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores using semiconductor elements
40.
In memory matrix multiplication and its usage in neural networks
A method for in memory computation of a neural network, the neural network having weights arranged in a matrix, includes previously storing the matrix in an associated memory device, receiving an input arranged in a vector and storing it in the memory device, and in-memory, computing an output of the network using the input and the weights.
G11C 15/00 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores
G11C 7/22 - Read-write [R-W] timing or clocking circuits; Read-write [R-W] control signal generators or management
An associative memory array includes a plurality of associative memory cells arranged in rows and columns where each first cell in a first row and in a first column has access to a content of a second cell in a second row in an adjacent column.
A device and method for facilitating orthogonal data transposition during data transfers to/from a processing array and a storage memory since the data words processed by the processing array (using computational memory cells) are stored orthogonally to how the data words are stored in storage memory. Thus, when data words are transferred between storage memory and the processing array, a mechanism orthogonally transposes the data words.
G06F 15/78 - Architectures of general purpose stored program computers comprising a single central processing unit
G11C 11/412 - Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements using semiconductor devices using transistors forming cells with positive feedback, i.e. cells not needing refreshing or charge regeneration, e.g. bistable multivibrator or Schmitt trigger using field-effect transistors only
A method includes determining a set of k extreme values of a dataset of elements in a constant time irrespective of the size of the dataset. The determining includes reviewing the values bit-by-bit, starting from the most significant bit, where bit n from each element of the dataset is reviewed at the same time.
G06N 3/04 - Architecture, e.g. interconnection topology
G06F 16/22 - Indexing; Data structures therefor; Storage structures
G06F 7/00 - Methods or arrangements for processing data by operating upon the order or content of the data handled
G06F 7/22 - Arrangements for sorting or merging computer data on continuous record carriers, e.g. tape, drum, disc
G06F 7/544 - Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using unspecified devices for evaluating functions by calculation
A method for an associative memory device includes storing a plurality of pairs of N-bit numbers A and B to be added together in columns of a memory array of the associative memory device, each pair in a column, each bit in a row of the column, and dividing each N-bit number A and B into groups containing M bits each, having group carry-out predictions for every group except a first group, the group carry-out predictions calculated for any possible group carry-in value, and, once the carry-out value for a first group is calculated, selecting the next group carry out value from the group carry-out predictions. The method also includes repeating the ripple selecting group carry-out values, until all group carry out values have been selected.
G06F 7/508 - Adding; Subtracting in bit-parallel fashion, i.e. having a different digit-handling circuit for each denomination with simultaneous carry generation for, or propagation over, two or more stages using carry look-ahead circuits
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
G11C 15/04 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores using semiconductor elements
An ultra low VDD memory cell has a ratioless write port. In some embodiments, the VDD operation level can be as low as the threshold voltage of NMOS and PMOS transistors of the cell.
H03K 19/0944 - Logic circuits, i.e. having at least two inputs acting on one output; Inverting circuits using specified components using semiconductor devices using field-effect transistors using MOSFET
G11C 11/412 - Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements using semiconductor devices using transistors forming cells with positive feedback, i.e. cells not needing refreshing or charge regeneration, e.g. bistable multivibrator or Schmitt trigger using field-effect transistors only
46.
DEDUPLICATION OF DATA VIA ASSOCIATIVE SIMILARITY SEARCH
A deduplication system includes a similarity searcher, a difference calculator, and a storage manager. The similarity searcher searches for a similar fingerprint in a database storing a plurality of local sensitive fingerprints, resembling a new fingerprint of a new block. The difference calculator computes a difference block between the input block and a similar block associated with the found similar fingerprint, and the storage manager updates the database with the new fingerprint and stores the difference block, if not empty, in a store. A method for deduplication includes searching in a database, storing a plurality of local sensitive fingerprints, a similar fingerprint, resembling a new fingerprint of a new block, calculating a difference block between the input block and a similar block associated with the similar fingerprint, if found, updating the database with the new fingerprint and storing the difference block, if it is not empty, in a storage unit.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
47.
Processing array device that performs one cycle full adder operation and bit line read/write logic features
A processing array that performs one cycle full adder operations. The processing array may have different bit line read/write logic that permits different operations to be performed.
A processing array that performs one cycle full adder operations. The processing array may have different bit line read/write logic that permits different operations to be performed.
G06F 7/501 - Half or full adders, i.e. basic adder cells for one denomination
H03K 19/21 - EXCLUSIVE-OR circuits, i.e. giving output if input signal exists at only one input; COINCIDENCE circuits, i.e. giving output only if all input signals are identical
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
G11C 7/12 - Bit line control circuits, e.g. drivers, boosters, pull-up circuits, pull-down circuits, precharging circuits, equalising circuits, for bit lines
G11C 7/18 - Bit line organisation; Bit line lay-out
G11C 7/22 - Read-write [R-W] timing or clocking circuits; Read-write [R-W] control signal generators or management
49.
Write data processing methods associated with computational memory cells
A write data processing method associated with computational memory cells formed as a memory/processing array provides the ability to shift data between adjacent bit lines in each section of the memory/processing array or the same relative bit lines in adjacent sections of the memory/processing array.
H03K 19/0944 - Logic circuits, i.e. having at least two inputs acting on one output; Inverting circuits using specified components using semiconductor devices using field-effect transistors using MOSFET
A similarity search system includes a database of original vectors, a hierarchical database of bins and a similarity searcher. The hierarchical database of bins is stored in an associative memory array, each bin identified by an order vector representing at least one original vector and the dimension of the order vector is smaller than the dimension of the original vector. The similarity searcher searches in the database for at least one similar bin whose order vector resembles an order vector representing a query vector and provides at least one original vector represented by the bin resembling the query vector.
A read and write data processing apparatus and method associated with computational memory cells formed as a memory/processing array (having a plurality of bit line sections) provides a mechanism to logically combine the computation results across multiple bit line sections in a section and across multiple sections, and transmit the combined result as an output of the processing array and/or store the combined result into one or more of those multiple bit line sections.
A memory device includes a non-destructive memory array that includes memory cells arranged in rows and columns. The array includes a plurality of word lines, first bit lines and second bit lines, a NOR gate per column Each word line activates memory cells in a row and thereby establishes an activated row. First bit lines and second bit lines connect memory cells in columns, each first bit line provides the result of a Boolean AND operation between data stored in the first activated row and data stored in the second activated row. Each second bit line provides the result of a Boolean NOR operation between data stored in the first activated row and data stored in the second activated row. Each per-column NOR gate is connected to the first and second bit lines of each column and compares data stored in the first activated row with data stored in the second activated row.
G06F 11/10 - Adding special bits or symbols to the coded information, e.g. parity check, casting out nines or elevens
H03K 19/20 - Logic circuits, i.e. having at least two inputs acting on one output; Inverting circuits characterised by logic function, e.g. AND, OR, NOR, NOT circuits
A processing array that performs one cycle full adder operations. The processing array may have different bit line read/write logic that permits different operations to be performed.
G06F 7/501 - Half or full adders, i.e. basic adder cells for one denomination
H03K 19/21 - EXCLUSIVE-OR circuits, i.e. giving output if input signal exists at only one input; COINCIDENCE circuits, i.e. giving output only if all input signals are identical
54.
Computational memory cell and processing array device using complementary exclusive or memory cells
A memory cell and processing array that has a plurality of memory are capable of performing logic functions, including an exclusive OR (XOR) or an exclusive NOR (XNOR) logic function. The memory cell may have a read port in which the digital data stored in the storage cell of the memory cell is isolated from the read bit line.
H03K 19/0948 - Logic circuits, i.e. having at least two inputs acting on one output; Inverting circuits using specified components using semiconductor devices using field-effect transistors using MOSFET using CMOS
H03K 19/21 - EXCLUSIVE-OR circuits, i.e. giving output if input signal exists at only one input; COINCIDENCE circuits, i.e. giving output only if all input signals are identical
H03K 19/20 - Logic circuits, i.e. having at least two inputs acting on one output; Inverting circuits characterised by logic function, e.g. AND, OR, NOR, NOT circuits
55.
Orthogonal data transposition system and method during data transfers to/from a processing array
A device and method for facilitating orthogonal data transposition during data transfers to/from a processing array and a storage memory since the data words processed by the processing array (using computational memory cells) are stored orthogonally to how the data words are stored in storage memory. Thus, when data words are transferred between storage memory and the processing array, a mechanism orthogonally transposes the data words.
G11C 11/41 - Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements using semiconductor devices using transistors forming cells with positive feedback, i.e. cells not needing refreshing or charge regeneration, e.g. bistable multivibrator or Schmitt trigger
G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
G06F 15/78 - Architectures of general purpose stored program computers comprising a single central processing unit
G11C 11/412 - Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements using semiconductor devices using transistors forming cells with positive feedback, i.e. cells not needing refreshing or charge regeneration, e.g. bistable multivibrator or Schmitt trigger using field-effect transistors only
A computational memory cell and processing array have a ratioless write port so that a write to the memory cell does not need to overcome the drive strength of a PMOS transistor that is part of the storage cell of the memory cell. The computational memory cell also may have a second read port that has an isolation circuit.
G11C 11/412 - Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements using semiconductor devices using transistors forming cells with positive feedback, i.e. cells not needing refreshing or charge regeneration, e.g. bistable multivibrator or Schmitt trigger using field-effect transistors only
57.
Pipeline architecture for bitwise multiplier-accumulator (MAC)
A unit for accumulating a plurality N of multiplied M bit values includes a receiving unit, a bit-wise multiplier and a bit-wise accumulator. The receiving unit receives a pipeline of multiplicands A and B such that, at each cycle, a new set of multiplicands is received. The bit-wise multiplier bit-wise multiplies bits of a current multiplicand A with bits of a current multiplicand B and to sum and carry between bit-wise multipliers. The bit-wise accumulator accumulates output of the bit-wise multiplier thereby to accumulate the multiplicands during the pipelining process.
G06F 9/38 - Concurrent instruction execution, e.g. pipeline, look ahead
G06F 7/544 - Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using unspecified devices for evaluating functions by calculation
G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
A write data processing apparatus and method associated with computational memory cells formed as a memory/processing array provides the ability to shift data between adjacent bit lines in each section of the memory/processing array or the same relative bit lines in adjacent sections of the memory/processing array. The memory/processing array has one or more sections and each section has its own unique set of “n” bit lines.
H03K 19/0944 - Logic circuits, i.e. having at least two inputs acting on one output; Inverting circuits using specified components using semiconductor devices using field-effect transistors using MOSFET
A read and write data processing apparatus and method associated with computational memory cells formed as a memory/processing array provides the ability for selected write data in a bit line section to be logically combined (e.g. logically ANDed) with the read result on a read bit line, as if the write data were the read data output of another computational memory cell being read during the read operation. When accumulation logic is implemented in the bit line sections, the implementation and utilization of additional read logic circuitry provides a mechanism for selected write data in a bit line section to be used as the data with which the read result on the read bit line accumulates, before the newly accumulated result is captured and stored in the bit line section's read register.
A system for performing cascading search includes an associative memory array, a controller, a similarity search processor and an exact match processor. The associative memory array stores a plurality of multiportion data vectors stored in at least one column of the associative memory array. Each vector has a first portion and a second portion which are aligned to each other in the column. The controller controls the associative memory array to perform a similarity search of a similarity query on the first portion and an exact search of an exact query on the second portion. The similarity match processor generates a match row including match bit indications aligned with each similarity matched column. The match row indicates which columns have first portions which match to the similarity query. The exact match processor outputs exact match columns from among the similarity matched columns which have second portions which match the exact query.
A memory cell that may be used for computation and processing array using the memory cell are capable to performing a logic operation including a boolean AND, a boolean OR, a boolean NAND or a boolean NOR. The memory cell may have a read port that has isolation circuits that isolate the data stored in the storage cell of the memory cell from the read bit line.
G11C 11/00 - Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor
G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
G11C 11/412 - Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements using semiconductor devices using transistors forming cells with positive feedback, i.e. cells not needing refreshing or charge regeneration, e.g. bistable multivibrator or Schmitt trigger using field-effect transistors only
G06F 15/78 - Architectures of general purpose stored program computers comprising a single central processing unit
A read register is provided that captures and stores the read result on a read bit line connected to a set of computational memory cells. The read register may be implemented in the set of computational memory cell to enable the logical XOR, logical AND, and/or logical OR accumulation of read results in the read register. The set of computational memory cells with the read register provides a mechanism for performing complex logical functions across multiple computational memory cells connected to the same read bit line.
A self-correcting memory device (SCMD) includes a non-destructive memory array that includes memory cells arranged in rows and columns that includes a storage section, a comparison section, a comparing element, a selective write unit and a row decoder. The storage section stores a first copy, a second copy and a third copy of a data item in physically separated columns. The comparison section temporarily stores the first copy in a first row and the second copy in a second row. The comparing element compares between bits of the first and second rows and provides at least one per bit change indication. The selective write unit receives at least one per bit change indication and fetches from the third copy a correct value for each bit having a positive bit change indication. The row decoder concurrently writes each correct value back to its bit location in the first and second copies.
G06F 11/10 - Adding special bits or symbols to the coded information, e.g. parity check, casting out nines or elevens
H03K 19/20 - Logic circuits, i.e. having at least two inputs acting on one output; Inverting circuits characterised by logic function, e.g. AND, OR, NOR, NOT circuits
An in-memory multiplier-accumulator includes a memory array, a multi-bit multiplier and a multi-bit layered adder. The memory array has a multiplicity of rows and columns, each column being divided into a plurality of bit line processors and each bit line processor operating on its associated pair of input values. The multi-bit multiplier utilizes each bit line processor to multiply the associated pair of input values in each bit line processor to generate multiplication results. The multi-bit layered adder accumulates the multiplication results of each column of bit line processors.
G11C 7/20 - Memory cell initialisation circuits, e.g. when powering up or down, memory clear, latent image memory
G11C 7/12 - Bit line control circuits, e.g. drivers, boosters, pull-up circuits, pull-down circuits, precharging circuits, equalising circuits, for bit lines
G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
G06F 7/544 - Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using unspecified devices for evaluating functions by calculation
68.
Systems and methods involving lock loop circuits, distributed duty cycle correction loop circuitry
A system, method and circuits are described that pertain to locked loop circuits, distributed duty cycle correction loop circuitry. In some embodiments, the system and circuit may involve or be configured for coupling with lock loop circuitry such as phase locked loop (PLL) circuitry and/or a delay locked loop (DLL) circuitry. For example, one illustrative implementation may include or involve a phase locked loop (PLL) with distributed duty cycle correction loop/circuitry.
H03L 7/081 - Automatic control of frequency or phase; Synchronisation using a reference signal applied to a frequency- or phase-locked loop - Details of the phase-locked loop provided with an additional controlled phase shifter
H03K 5/156 - Arrangements in which a continuous pulse train is transformed into a train having a desired pattern
G11C 7/22 - Read-write [R-W] timing or clocking circuits; Read-write [R-W] control signal generators or management
A non-destructive memory array implements a full adder. The array includes a column connected by a bit line and a full adder unit. The column stores a first bit in a first row of the bit line, a second bit in a second row of the bit line, and an inverse of a carry-in bit in a third row of the bit line. The full adder unit stores, in the second and third rows of the bit line, a sum bit and a carry out bit output, respectively, of adding the first bit, the second bit and the carry-in bit. The full adder unit does not overwrite any of the bits when a full adder table indicates that the sum bit and the carry out bit are equivalent to the second bit and the carry-in bit.
G06F 17/11 - Complex mathematical operations for solving equations
G06F 7/48 - Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using unspecified devices
G06F 7/501 - Half or full adders, i.e. basic adder cells for one denomination
G06F 7/506 - Adding; Subtracting in bit-parallel fashion, i.e. having a different digit-handling circuit for each denomination with simultaneous carry generation for, or propagation over, two or more stages
70.
Method for min-max computation in associative memory
A method for finding an extreme value among a plurality of numbers in an associative memory includes creating a spread-out representation (SOR) for each number of the plurality of numbers, storing each SOR in a column of the associative memory array and performing a horizontal bit-wise Boolean operation on rows of the associative memory array to produce an extreme SOR (ESOR) having the extreme value. A system for finding an extreme value includes an associative memory array to store the plurality of numbers, each number storable in a column; a spread-out representation (SOR) creator to create a SOR for each number of the plurality of numbers and to store each SOR in a column of the associative memory array, and an extreme SOR (ESOR) finder to find an extreme value using a horizontal bit-wise Boolean operation on rows of the associative memory array storing bits of the SORs.
G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
G11C 15/04 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores using semiconductor elements
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
G06F 12/1027 - Address translation using associative or pseudo-associative address translation means, e.g. translation look-aside buffer [TLB]
71.
Results processing circuits and methods associated with computational memory cells
A read and write data processing apparatus and method associated with computational memory cells formed as a memory/processing array (having a plurality of bit line sections) provides a mechanism to logically combine the computation results across multiple bit line sections in a section and across multiple sections, and transmit the combined result as an output of the processing array and/or store the combined result into one or more of those multiple bit line sections.
A method for use in an associative memory device when multiplying by a sparse matrix includes storing only non-zero elements of the sparse matrix in the associative memory device as multiplicands. The storing includes locating the non-zero elements in computation columns of the associative memory device according to linear algebra rules along with their associated multiplicands such that a multiplicand and a multiplier of each multiplication operation to be performed are stored in a same computation column. The locating locates one of the non-zero elements in more than one computation column if one of the non-zero elements is utilized in more than one multiplication operation.
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G06F 5/01 - Methods or arrangements for data conversion without changing the order or content of the data handled for shifting, e.g. justifying, scaling, normalising
G06F 7/58 - Random or pseudo-random number generators
A system includes an associative memory array and a concurrent adder. The memory array includes a plurality of sections, where each section includes cells arranged in rows and columns. The memory array stores bit j from a first multi-bit number and bit j from a second multi-bit number in a same column in section j. The concurrent adder performs, in parallel, multi-bit add operations of P pairs of multi-bit operands stored in columns of a memory array. Each pair of the P pairs is stored in a different column of the array and each add operation occurs in its associated different column.
G06F 7/508 - Adding; Subtracting in bit-parallel fashion, i.e. having a different digit-handling circuit for each denomination with simultaneous carry generation for, or propagation over, two or more stages using carry look-ahead circuits
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
G11C 15/04 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores using semiconductor elements
A method for an associative memory device includes replacing a set of three multi-bit binary numbers P, Q and R, stored in the associative memory device, with two multi-bit binary numbers X and Y, also stored in the associative memory device, wherein a sum of the binary numbers P, Q and R is equal to a sum of the binary numbers X and Y. A system includes an associative memory array having rows and columns and a multi-bit multiplier. Each column of the array stores two multi-bit binary numbers to be multiplied. The multi-bit multiplier multiplies, in parallel, the two multi-bit binary numbers per column by concurrently processing all bits of partial products generated by the multiplier. The multiplier performs the processing without any carry propagation delay when adding all but the last two partial products.
A method for a neural network includes concurrently calculating a distance vector between an output feature vector describing an unclassified item and each of a plurality of qualified feature vectors, each describing one classified item out of a collection of classified items. The method includes concurrently computing a similarity score for each distance vector and creating a similarity score vector of the plurality of computed similarity scores. A system for a neural network includes an associative memory array, an input arranger, a hidden layer computer and an output handler. The input arranger manipulates information describing an unclassified item stored in the memory array. The hidden layer computer computes a hidden layer vector. The output handler computes an output feature vector and concurrently calculates a distance vector between an output feature vector and each of a plurality of qualified feature vectors, and concurrently computes a similarity score for each distance vector.
A method for an associative memory device includes dividing a multi-bit mantissa A of a number X to a plurality of smaller partial mantissas Aj, offline calculating a plurality of partial exponents F(Aj) for each possible value of each partial mantissa Aj and storing the plurality of partial exponents F(Aj) in a look up table (LUT) of the associative memory device. A system includes an associative memory array to store a plurality of partial mantissas Ai of a mantissa A of a number X and an exponent calculator to utilize the partial mantissas to compute e in the power of X.
A system includes a non-destructive associative memory array and a predictor, a selector and a summer. The memory array includes a plurality of sections, each section includes cells arranged in rows and columns, to store bit j from a first multi-bit number and bit j from a second multi-bit number in a same column in section j. The predictor generally concurrently predicts a plurality of carry out values in each of the sections and the selector selects one of the predicted carry out values for all bits. The summer generally concurrently, for all bits, calculates a sum of the multi-bit numbers using the selected carry-out values.
G06F 7/508 - Adding; Subtracting in bit-parallel fashion, i.e. having a different digit-handling circuit for each denomination with simultaneous carry generation for, or propagation over, two or more stages using carry look-ahead circuits
A method for finding an extreme value among a plurality of numbers in an associative memory includes creating a spread-out representation (SOR) for each number of the plurality of numbers, storing each SOR in a column of the associative memory array and performing a horizontal bit-wise Boolean operation on rows of the associative memory array to produce an extreme SOR (ESOR) having the extreme value. A system for finding an extreme value includes an associative memory array to store the plurality of numbers, each number storable in a column; a spread-out representation (SOR) creator to create a SOR for each number of the plurality of numbers and to store each SOR in a column of the associative memory array, and an extreme SOR (ESOR) finder to find an extreme value using a horizontal bit-wise Boolean operation on rows of the associative memory array storing bits of the SORs.
G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
G11C 15/04 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores using semiconductor elements
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
G06F 12/1027 - Address translation using associative or pseudo-associative address translation means, e.g. translation look-aside buffer [TLB]
A method and a system for selecting items one by one from a set of items in an associative memory array includes determining a density of the set, if the density is sparse, repeatedly performing an extreme item select (EIS) method to select a next one of the elected items from the set and removing the next one from the set to create a next set, and if the density is not sparse, performing a next index select (NIS) method to create a linked list of the elected items and to repeatedly select a next elected item from the set. An associative memory array includes a plurality of associative memory cells arranged in rows and columns where each first cell in a first row and in a first column has access to a content of a second cell in a second row in an adjacent column.
A method for machine learning includes extracting features from a training set of inputs, wherein each input generates a feature set and each the feature set forms a neural network key. The method includes arranging the keys in an in-memory computational layer such that the distance between any pair of keys corresponding to similar inputs is as close as possible while keys for a pair of dissimilar inputs have differing values as far apart as possible, wherein each of the keys has a fixed size. The method also includes searching through the dataset using an in-memory K-nearest neighbor unit to find K keys similar to a query key, the searching occurring in a constant amount of time as a function of the fixed size and irrespective of a size of the dataset.
A system for natural language processing includes a memory array and a processor. The memory array is divided into a similarity section storing a plurality of feature vectors, a SoftMax section in which to determine probabilities of occurrence of the feature vectors, a value section storing a plurality of modified feature vectors, and a marker section. The processor activates the array to perform parallel operations in each column indicated by the marker section: a similarity operation in the similarity section between a vector question and feature vectors stored in indicated columns; a SoftMax operation in the SoftMax section to determine an associated SoftMax probability value for indicated feature vectors; a multiplication operation in the value section to multiply the associated SoftMax value by modified feature vectors stored in indicated columns; and a vector sum in the value section to accumulate an attention vector of output of the multiplication operation.
Systems and methods of synchronous memories and synchronous memory operation are disclosed. According to one illustrative implementation, a memory device is disclosed comprising memory circuitry having a memory output, the memory circuitry including a sense amplifier having a first output and a second output, a first data path coupled to the first output of the sense amplifier, the first data path including 2 latches/registers, and a second data path coupled to the second output of the sense amplifier, the second data path including a plurality latches/registers. In further implementations, various control circuitry, connections and control signals may be utilized to operate the latches/registers in the first and second data paths according to specified configurations, control, modes, latency and/or timing domain information, to achieve, for example, pipelined output latching and/or double data rate output.
G11C 11/4096 - Input/output [I/O] data management or control circuits, e.g. reading or writing circuits, I/O drivers or bit-line switches
G11C 8/18 - Address timing or clocking circuits; Address control signal generation or management, e.g. for row address strobe [RAS] or column address strobe [CAS] signals
G11C 29/02 - Detection or location of defective auxiliary circuits, e.g. defective refresh counters
86.
SPARSE MATRIX MULTIPLICATION IN ASSOCIATIVE MEMORY DEVICE
A method for multiplying a first sparse matrix by a second sparse matrix in an associative memory device includes storing multiplicand information related to each non-zero element of the second sparse matrix in a computation column of the associative memory device; the multiplicand information includes at least a multiplicand value. According to a first linear algebra rule, the method associates multiplier information related to a non-zero element of the first sparse matrix with each of its associated multiplicands, the multiplier information includes at least a multiplier value. The method concurrently stores the multiplier information in the computation columns of each associated multiplicand. The method, concurrently on all computation columns, multiplies a multiplier value by its associated multiplicand value to provide a product in the computation column, and adds together products from computation columns, associated according to a second linear algebra rule, to provide a resultant matrix.
A method for multiplying a first sparse matrix by a second sparse matrix in an associative memory device includes storing multiplicand information related to each non-zero element of the second sparse matrix in a computation column of the associative memory device; the multiplicand information includes at least a multiplicand value. According to a first linear algebra rule, the method associates multiplier information related to a non-zero element of the first sparse matrix with each of its associated multiplicands, the multiplier information includes at least a multiplier value. The method concurrently stores the multiplier information in the computation columns of each associated multiplicand. The method, concurrently on all computation columns, multiplies a multiplier value by its associated multiplicand value to provide a product in the computation column, and adds together products from computation columns, associated according to a second linear algebra rule, to provide a resultant matrix.
A memory cell that may be used for computation and processing array using the memory cell are capable to performing a logic operation including a boolean AND, a boolean OR, a boolean NAND or a boolean NOR. The memory cell may have a read port that has isolation circuits that isolate the data stored in the storage cell of the memory cell from the read bit line.
G11C 8/08 - Word line control circuits, e.g. drivers, boosters, pull-up circuits, pull-down circuits, precharging circuits, for word lines
G11C 8/16 - Multiple access memory array, e.g. addressing one storage element via at least two independent addressing line groups
G11C 8/18 - Address timing or clocking circuits; Address control signal generation or management, e.g. for row address strobe [RAS] or column address strobe [CAS] signals
G11C 11/404 - Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements using semiconductor devices using transistors forming cells needing refreshing or charge regeneration, i.e. dynamic cells with charge regeneration common to a multiplicity of memory cells, i.e. external refresh with one charge-transfer gate, e.g. MOS transistor, per cell
89.
COMPUTATIONAL MEMORY CELL AND PROCESSING ARRAY DEVICE USING THE MEMORY CELLS FOR XOR AND XNOR COMPUTATIONS
A memory cell and processing array that has a plurality of memory are capable of performing logic functions, including an exclusive OR (XOR) or an exclusive NOR (XNOR) logic function. The memory cell may have a read port in which the digital data stored in the storage cell of the memory cell is isolated from the read bit line.
G11C 8/08 - Word line control circuits, e.g. drivers, boosters, pull-up circuits, pull-down circuits, precharging circuits, for word lines
G11C 8/16 - Multiple access memory array, e.g. addressing one storage element via at least two independent addressing line groups
G11C 8/18 - Address timing or clocking circuits; Address control signal generation or management, e.g. for row address strobe [RAS] or column address strobe [CAS] signals
G11C 11/404 - Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements using semiconductor devices using transistors forming cells needing refreshing or charge regeneration, i.e. dynamic cells with charge regeneration common to a multiplicity of memory cells, i.e. external refresh with one charge-transfer gate, e.g. MOS transistor, per cell
A method to add a first one bit variable with a second one bit variable and a carry-in bit, to generate a sum bit and a carry-out bit, the method includes initiating the sum bit to the value of the second one bit variable, initiating the carry-out bit to a value of the carry-in bit and modifying the sum bit and the carry-out bit if a comparison of a sequence of the first one bit variable, the second one bit variable and an inverse value of the carry-in bit matches one of a predefined set of a change trigger sequences.
G06F 17/11 - Complex mathematical operations for solving equations
G06F 7/48 - Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using unspecified devices
G06F 7/501 - Half or full adders, i.e. basic adder cells for one denomination
91.
Computational memory cell and processing array device using memory cells
A memory cell that may be used for computation and processing array using the memory cell are capable to performing a logic operation including a boolean AND, a boolean OR, a boolean NAND or a boolean NOR. The memory cell may have a read port that has isolation circuits that isolate the data stored in the storage cell of the memory cell from the read bit line.
G06F 15/78 - Architectures of general purpose stored program computers comprising a single central processing unit
G11C 11/412 - Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements using semiconductor devices using transistors forming cells with positive feedback, i.e. cells not needing refreshing or charge regeneration, e.g. bistable multivibrator or Schmitt trigger using field-effect transistors only
92.
Computational memory cell and processing array device using memory cells
A memory cell that may be used for computation and processing array using the memory cell are capable to performing a logic operation including a boolean AND, a boolean OR, a boolean NAND or a boolean NOR. The memory cell may have a read port that has isolation circuits that isolate the data stored in the storage cell of the memory cell from the read bit line.
G11C 11/00 - Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor
G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
G11C 11/412 - Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements using semiconductor devices using transistors forming cells with positive feedback, i.e. cells not needing refreshing or charge regeneration, e.g. bistable multivibrator or Schmitt trigger using field-effect transistors only
G06F 15/78 - Architectures of general purpose stored program computers comprising a single central processing unit
A memory cell that may be used for computation and processing array using the memory cell are capable to performing a logic operation including a boolean AND, a boolean OR, a boolean NAND or a boolean NOR. The memory cell may have a read port that has isolation circuits that isolate the data stored in the storage cell of the memory cell from the read bit line.
G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
G11C 11/412 - Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements using semiconductor devices using transistors forming cells with positive feedback, i.e. cells not needing refreshing or charge regeneration, e.g. bistable multivibrator or Schmitt trigger using field-effect transistors only
G06F 15/78 - Architectures of general purpose stored program computers comprising a single central processing unit
A memory cell and processing array that has a plurality of memory are capable of performing logic functions, including an exclusive OR (XOR) or an exclusive NOR (XNOR) logic function. The memory cell may have a read port in which the digital data stored in the storage cell of the memory cell is isolated from the read bit line.
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
G11C 7/18 - Bit line organisation; Bit line lay-out
G11C 15/04 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores using semiconductor elements
95.
Computational memory cell and processing array device using the memory cells for XOR and XNOR computations
A memory cell and processing array that has a plurality of memory are capable of performing logic functions, including an exclusive OR (XOR) or an exclusive NOR (XNOR) logic function. The memory cell may have a read port in which the digital data stored in the storage cell of the memory cell is isolated from the read bit line.
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
G11C 7/18 - Bit line organisation; Bit line lay-out
G11C 15/04 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores using semiconductor elements
A multiple instruction, multiple data memory device includes a memory array with several sections, one or more multiplexers between the sections and a decoder. Each section has memory cells arranged in rows and columns. The cells in a row are connected by a read enabled word line and by a write enabled word line. The decoder includes a decoder memory array which generally simultaneously activates a plurality of read enabled word lines in several sections, a plurality of write enabled word lines in several sections and one or more multiplexers. The decoder memory array includes several bit lines oriented perpendicularly to and connected to the rows of the memory array. A method of activating in-memory computation using several bit lines of a decoder memory array, connected to rows of the memory array to simultaneously activate several read enabled word lines, several write enabled word lines and one or more multiplexers.
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
G11C 15/04 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores using semiconductor elements
A memory device includes a memory array of non-volatile memory cells arranged in rows and columns and responder signal circuitry. The responder signal circuitry performs a calculation on a row of the memory array and generates a responder signal indicating that there is at least one cell in the row having a predefined value.
G11C 15/00 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores
G11C 15/04 - Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores using semiconductor elements
98.
Systems and methods involving lock-loop circuits, clock signal alignment, phase-averaging feedback clock circuitry
Systems and methods associated with reducing clock skew are disclosed. In some exemplary embodiments, there is provided circuitry associated with lock loop circuits such as a phase lock loop (PLL). Such circuitry may comprise output clock tree circuitry and phase averaging circuitry. In other exemplary embodiments, there is provided circuitry associated with delay lock loop (DLL) circuits. Such circuitry may comprise output clock tree circuitry and/or phase averaging circuitry.
H03K 5/15 - Arrangements in which pulses are delivered at different times at several outputs, i.e. pulse distributors
H03L 7/081 - Automatic control of frequency or phase; Synchronisation using a reference signal applied to a frequency- or phase-locked loop - Details of the phase-locked loop provided with an additional controlled phase shifter
H03K 5/135 - Arrangements having a single output and transforming input signals into pulses delivered at desired time intervals by the use of time reference signals, e.g. clock signals
H03L 7/08 - Automatic control of frequency or phase; Synchronisation using a reference signal applied to a frequency- or phase-locked loop - Details of the phase-locked loop
H03L 7/16 - Indirect frequency synthesis, i.e. generating a desired one of a number of predetermined frequencies using a frequency- or phase-locked loop
H03K 5/00 - Manipulation of pulses not covered by one of the other main groups of this subclass
99.
Systems and methods involving control-I/O buffer enable circuits and/or features of saving power in standby mode
A method of operating a clock frequency detected control-i/o buffer enable circuit in a semiconductor device uses control I/O buffer enable circuitry and/or features of saving power in standby mode. The method may provide low standby power consumption, such as providing low standby power consumption in high-speed synchronous SRAM and RLDRAM devices.
G11C 7/22 - Read-write [R-W] timing or clocking circuits; Read-write [R-W] control signal generators or management
G11C 11/4074 - Power supply or voltage generation circuits, e.g. bias voltage generators, substrate voltage generators, back-up power, power control circuits
A method includes determining a set of k extreme values of a dataset of elements in a constant time irrespective of the size of the dataset. A method creates a set of k indicators, each indicator associated with one multi-bit binary number in a large dataset of multi-bit binary numbers. The method includes arranging the multi-bit binary numbers such that each bit n of each said multi-bit binary number is located in a different row n of an associative memory array, starting from a row storing a most significant bit (MSB), adding an indicator to the set for each multi-bit binary number having a bit with an extreme value in the row and continuing the adding until said set contains k indicators.
