A Heterogeneous Multicore System on Chip for Energy Efficient Brain Inspired Computing

Abstract

Convolutional neural networks (CNNs) have revolutionized computer vision, speech recognition, and other fields requiring strong classification capabilities. These strengths make CNNs appealing in edge node Internet-of-Things (IoT) applications requiring near-sensors processing. Specialized CNN accelerators deliver significant performance per watt and satisfy the tight constraints of deeply embedded devices, but they cannot be used to implement arbitrary CNN topologies or nonconventional sensory algorithms where CNNs are only a part of the processing stack. A higher level of flexibility is desirable for next generation IoT nodes. Here, we present Mia Wallace, a 65-nm system-on-chip integrating a near-threshold parallel processor cluster tightly coupled with a CNN accelerator: it achieves peak energy efficiency of 108 GMAC/s/W at 0.72 V and peak performance of 14 GMAC/s at 1.2 V, leaving 1.2 GMAC/s available for general-purpose parallel processing.