JLPEA Special Issue: Energy-Aware Neuromorphic Hardware

Special Issue: Energy-Aware Neuromorphic Hardware
in Journal of Low Power Electronics and Applications

During recent years, researchers throughout academia and industry have been advancing the theory, operation, and applications of neuromorphic computing systems. Recent interest in neuromorphic computing systems stems from its superior and rapidly advancing performance at tasks such as image recognition, learning of complex intelligent behaviors, and large-scale information retrieval problems such as intelligent web search. However, to attain the benefits of neuromorphic computing, high computational and energy-consumption demands of the underlying processing, interconnect, and memory devices on which software-based neuromorphic computing executes has become an intense focus of government, industry, and academic research. Innovative hardware implementations are sought to attain throughput goals within area, security, and energy constraints for orders of magnitude improvements via innovations across the hardware stack.

This Special Issue of the JLPEA is dedicated to advances in all aspects of Energy-Aware Neuromorphic Hardware. We invite original submissions advancing device, circuits, and hardware architectures of neuromorphic computing systems.