Spike-based analog-digital neuromorphic information processing system for sensor applications

Abstract

A spiking-neuron-based system that combines analog and digital multi-processor implementations for the bio-inspired processing of sensors is reported. This combination allows creating a powerful bio-inspired multiple-input sensor processing system for environment perception applications. The analog front-end encodes the input signal in a signed spike representation, which is further processed by means of a digital Spiking Neural Network (SNN) on a Single-Instruction Multiple-Data (SIMD) multiprocessor. The spike distribution for both systems is based on Address-Event Representation (AER) scheme, asynchronous for the Analog Pre-Processor (APP) and synchronous for the Digital Multi-Processor (DMP), synchronized by means of an AER transceiver. A proof-of-concept application of the system being able to process sensory information has been demonstrated. The system utilizes 30-neurons emulated by the DMP to process spike-encoded information provided by its analog counterpart, enabling the feature extraction of the input signal. The frequency detection capability of the system is experimentally reported.