CPSnB: Compressing and Processing Spatial Similarity near Memory Bank for DNNs

Near memory bank processing (NMBP) architecture only benefits memory-bound operations of DNNs in terms of energy consumption. Drawing on the insight that data compression can reduce the compute density of operators, transforming compute-bound operations into memory-bound operations, We propose CPSnB, a NMBP architecture combined with preserving numerical jump-spatial similarity compression (PNJ-SSC) method. CPSnB provides a tiling strategy for optimizing operators of different DNN models. Compared to the systolic host-side accelerator and existing dense and sparse NMBP, CPSnB significantly reduces energy consumption. Analysis of the experimental results indicates that a 60% compression ratio of activation can enhance the versatility of CPSnB in processing DNN operators to 22.3 times.