A 1.40mm2 141mW 898GOPS sparse neuromorphic processor in 40nm CMOS

Abstract

Sparsity is a brain-inspired property that enables a significant reduction in workload and power dissipation of deep learning. This work presents a 1.40mm2 40nm CMOS sparse neuromorphic processor that implements a two-layer convolutional restricted Boltzmann machine (CRBM) for inference and a support vector machine (SVM) classifier. The processor incorporates sparse convolvers to realize sparsity-proportional workload reduction. The architecture is parallelized along a non-sparse dimension to minimize stalling. At 0.9V and 240MHz, the processor achieves an effective 898.2GOPS performance, dissipating 140.9mW. Using sparsity, we reduce the workload, datapath power consumption and area by 3.4×, 3.3× and 1.74×, respectively. The design uses latch-based memory to reduce area and dynamic clock gating to save power.