DEEP NEURAL NETWORK (DNN) ACCELERATORS WITH WEIGHT LAYOUT REARRANGEMENT

Abrégé

An DNN accelerator includes a DMA engine that can rearrange weight data layout. The DMA engine may read a weight tensor from a memory (e.g., DRAM). The weight tensor includes weights arranged in a 3D matrix. The DMA engine may partition the weight tensor into a plurality of virtual banks based on a structure of a PE array, e.g., based on the number of activated PE columns in the PE array. Then the DMA engine may partition a virtual bank into a plurality of virtual sub-banks. The DMA engine may also identify data blocks from different ones of the plurality of virtual sub-banks. A data block may include a plurality of input channels and may have a predetermined spatial size and storage size. The DMA engine form a linear data structure by interleaving the data blocks. The DMA engine can write the linear data structure into another memory (e.g., SRAM).

Classes IPC  ?

• G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
• G06F 13/28 - Handling requests for interconnection or transfer for access to input/output bus using burst mode transfer, e.g. direct memory access, cycle steal