An Accelerator for Sparse Convolutional Neural Networks Leveraging Systolic General Matrix-matrix Multiplication

Abstract

This article proposes a novel hardware accelerator for the inference task with sparse convolutional neural networks (CNNs) by building a hardware unit to perform Image to Column ( Im2Col ) transformation of the input feature map coupled with a systolic-array-based general matrix-matrix multiplication (GEMM) unit. Our design carefully overlaps the Im2Col transformation with the GEMM computation to maximize parallelism. We propose a novel design for the Im2Col unit that uses a set of distributed local memories connected by a ring network, which improves energy efficiency and latency by streaming the input feature map only once. The systolic-array-based GEMM unit in the accelerator can be dynamically configured as multiple GEMM units with square-shaped systolic arrays or as a single GEMM unit with a tall systolic array. This dynamic reconfigurability enables effective pipelining of Im2Col and GEMM operations and attains high processing element utilization for a wide range of CNNs. Further, our accelerator is sparsity aware, improving performance and energy efficiency by effectively mapping the sparse feature maps and weights to the processing elements, skipping ineffectual operations and unnecessary data movements involving zeros. Our prototype, SPOTS, is on average 2.16 \( \times \) , 1.74 \( \times \) , and 1.63 \( \times \) faster than Gemmini, Eyeriss, and Sparse-PE, which are prior hardware accelerators for dense and sparse CNNs, respectively. SPOTS is also 78 \( \times \) and 12 \( \times \) more energy-efficient when compared to CPU and GPU implementations, respectively.