Weight-Reconfigurable Neuromorphic Computing Systems for Analog Signal Integration

Abstract

Owing to the necessity of high computation amounts has emerged, interest in a neuromorphic computing system has significantly increased as a compelling alternative to conventional CMOS technology. This paper presents a neuromorphic hardware algorithm to finely reconfigure multi-input signal processing, which can be implemented as an advanced processor for diverse external information, and the hydrogen explosion risk assessment system is demonstrated as a proof of concept. Hydrogen concentration and temperature are used as sensory inputs for the signal integration and the precise values of them are determined by offsetting the effect of temperature on the electrical signal from the hydrogen sensor through a sensor circuit. Each signal is then updated by the weight control circuit and converted into a postsynaptic current to represent the hydrogen explosion risk using a multi-input artificial synapse. This simplicity of the circuitry renders the fabrication of all components and circuits compatible with simple inkjet printing methods, enabling cost-effective and high-throughput manufacturing. Additionally, the real-time demonstration of the neuromorphic computing system is successfully conducted, offering insights into the practical application of neuromorphic computing. A neuromorphic hardware algorithm to reconfigure multi-input signal processing and the hydrogen explosion risk assessment system is demonstrated by composing a sensor circuit, weight control circuit, and processing synapse. The inkjet printing-based fabrication exhibits cost-effective and high-throughput manufacturing. The real-time demonstration is successfully conducted, offering insights into the practical application of neuromorphic computing. image