Neuromorphic Cellular Neural Network Processor for Intelligent Internet-of-Things

Abstract

We discuss the architecture, implementation and testing of a neuromorphic Cellular Neural Network (CNN) processor for intelligent IoT devices. The processor is based on a simplicial piecewise linear CNN architecture that allows implementation of linear and nolinear CNNs. A linear array of 64 processing element (PE) with column-shared computation resources, tightly coupled to two data memory caches was synthesized and fabricated in a 55nm CMOS technology using custom layout libraries. The fabricated chip achieves an overall performance of 2.95 TOPS/W with dynamic energy dissipation efficiency of 86.4fJ per OP at V=500mV. The processor can implement different types of processing on 2D data arrays, such as gray-scale morphology, gradient flow, median filters, and approximate Gaussian filters, among others.