Spiking Neural Network Learning Algorithms For Neuromorphic Hardware Efficiency

Abstract

Neuromorphic computing can be termed a revolution in the field of artificial intelligence, whereby we attempt to go beyond the conventional Von Neumann architecture in order to emulate the efficient architecture of the human brain. Spiking neural networks (SNNs) provide the foundation for this revolution, whereby the computation is carried out using discrete impulses referred to as spikes. Nevertheless, in order to optimize the performance of neuromorphic computing hardware, a careful coordination between the learning algorithms and the substrate is essential. In this paper, carry out an analysis of the SNN learning algorithms that have been optimized for execution on neuromorphic computing devices. Analyze the hardware limitations of modern-day architectures and the approaches used in circumventing them, such as on-chip memory, bandwidth, and routing limitations. Through the exploration of interactions between temporal gradient descent algorithms, biological synapse plasticity principles, and the conversion mechanisms from artificial to spiking networks, the current research develops a coherent approach to achieving tradeoffs between task performance and physical energy savings. Based on publicly available benchmark data sets and open-source software validation suites, empirically prove that careful algorithm design can result in significant energy savings. In particular, the proposed methodology provides 42% savings in latency and 35% energy efficiency compared to typical baseline SNNs, while maintaining 97.4% accuracy of the task. The ultimate conclusion is that the hardware-software co-design plays a key role in advancing the development of real-time and low-power edge-AI systems. The current paper is an architectural guide for scientists who strive to increase computing efficiency while staying within tight power budget constraints of future intelligent mobile networks, autonomous sensors, and automated systems.