Area Efficiency in Neuromorphics on Constrained Devices

Abstract

Resource-constrained devices form the foundation of the Internet of Things (IoT) era. Because IoT aims to create highly interconnected environments using small devices with limited computational, energy, and area resources, numerous control systems have been designed to operate efficiently as IoT edge devices while minimizing power and hardware overhead. Recently, researchers have explored integrating machine learning into control systems built on such constrained platforms. However, achieving low power consumption and minimal area utilization while maintaining high application performance remains a significant challenge. Spiking neuromorphic computing (SNC) is an emerging paradigm well suited to resource-constrained devices and a range of emerging applications. In addition to enabling machine-learning capabilities, SNC significantly reduces energy consumption. For instance, low-energy memory technologies such as memristors are commonly employed to support power-efficient operation while further reducing system area. Overall, SNC is expected to deliver computational efficiency comparable to deep learning, while operating within the strict power and resource limitations of constrained devices.