Spiking Neural Network E-Nose classifier chip

Abstract

Hardware E-Nose system classification is a challenging task. This paper presents our system architecture for chemical classifiers, with our recently developed Sampling Spiking Neural Network (SSNN) approach. The SSNN architecture is implemented on a 0.5 um CMOS technology tiny-chip designed to work in conjunction with a 256K external SRAM memory. It handles the routing of spike signals among 32,000 synapses and 255 neurons. At the same time, it tracks and records learning statistics. The chip can be used in parallel with other SSNN co-processors for very large systems. Experimental measurements using the Cyranose 320 sensor array and the SSNN-1 classifier are presented and results compare favorably to other E-Nose classification systems. The SSNN-1 is unique in its minimal yet powerful design with on-chip learning and parallel monitoring to detect binary odor patterns with high noise environment.