Irregular Operation-Unit-based Compression for Non-Volatile Computation-in-Memory Accelerator

Irregular pruning significantly enhances the sparsity of deep neural networks (DNNs) and reduces computational demands. However, the irregular sparse weight matrices can diminish the benefits of pruning when implemented in non-volatile computational-in-memory (CIM) accelerators, which relay on dense matrix-vector multiplications. In this work, we propose an irregular operation-unit-based (OU-based) compression method for nonvolatile CIM. We transform the compression problem into a fixed-size clustering task by clustering zero-columns through the rearrangement of matrix rows, followed by their elimination. This approach significantly reduces the number of required non-volatile memory (NVM) macros by up to 2.3x, while compacting the irregular non-zero data for efficient mapping onto non-volatile CIM. We achieve a compression ratio of up to 89% for irregularly pruned weights. Furthermore, we evaluate the mapping of the compacted weights onto nonvolatile CIM accelerator with OU size of 2x128 and 4x128. This work can achieve area efficiency of 0.166 TOPS/mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and improve area efficiency up to 3.1x compare to state-of-art works.