MSCA: A Multi-Grained Sparse Convolution Accelerator for DNN Training

Training deep neural networks (DNNs) on edge devices is appealing for its adaptability and privacy benefits, but it faces challenges due to the limited resources and energy available on edge devices. In this paper, we propose MSCA, a Multigrained Sparsity Convolution Accelerator. MSCA exploits both coarse-grained and fine-grained sparsity during the DNN training phases through two types of well-designed units. Experimental results show that MSCA implemented on FPGA achieves 218.03 GOPS throughput, 39.8 GOPS/W energy efficiency, and 4.0-6.2x speedup over dense accelerators for training VGG-8 and ResNet-10 on the CIFAR-10 and SVHN datasets.