APCPU: Adaptive-Pooling Compression Processing Unit for Energy-Efficient DNNs Processing

Integrating compression in the multiply-and-accumulate (MAC) path can significantly improve the energy efficiency of DNN operators. However, existing unstructured sparse compression (USSC) methods struggle to effectively compress activations with low sparsity. Computing core processing USSC face challenges such as load imbalance and complex index control circuit design. Based on insights into local spatial correlation, a block-wise adaptive-pooling compression (APC) method is proposed to achieve a high compression ratio for activations. Furthermore, this paper proposes an APCPU to integrate APC into the MAC path with minimal overhead, facilitating highly energy-efficient sparse processing of DNN operators. Leveraging a hybrid data flow design to achieve load balancing results in speedups of 1.25x to 1.33x. The experiment results show that the APCPU achieves energy savings of 1.35x and 1.27x compared to JPZ-PU, and 2.63x and 2.71x compared to CSC-PU when evaluated on AlexNet and Bert.