FPGA-Based Spiking Neural Networks

Abstract

This chapter explores the development and application of Spiking Neural Networks (SNNs) on Field-Programmable Gate Arrays (FPGAs), tracing their evolution since the debut of FPGAs in mid-1980s. It begins by examining the historical growth of FPGAs, emphasizing their role in developing complex neural network architectures. The narrative then charts the advancement of SNN designs on FPGAs, from early experiments to modern-day applications, spotlighting significant technological milestones and breakthroughs. The main emphasis is on the design and implementation strategies for SNNs on FPGAs, incorporating the latest research aimed at optimizing hardware use and computational efficiency. The chapter outlines effective techniques for mapping SNN models onto FPGA resources. Discussions include computational models of biological neurons on FPGAs, designing SNN accelerators to harness FPGA’s parallel processing capabilities, implementing SNN simulators, time-multiplexed neuronal networks, large SNN architectures on FPGA, and self-trainable neural architectures. This comprehensive blend of concepts and practical methodologies sets the foundation for designing modern SNNs that can be adapted for a range of advanced applications.