Event-driven spiking neural networks using asynchronous-logic network-on-chip routers in field programmable gate array (FPGA) - Wu, Si (Nanyang Technological University, 2023)
With the continuous development of deep learning, the scientific community continues to propose new neural network architectures based on brain-inspired research. So far, the architecture of neural networks has changed over three generations. The neural network-Spiking Neural Network (SNN) has aroused great interest among researchers. Compared with the second-generation neural network, SNN uses spikes to process information. The neuronal units in SNNs are only active when they receive or emit spikes, so it is event-driven, thus making it energy-efficient. This dissertation investigates the low power dissipation characteristics of the SNN and proposes effective techniques to reduce the power dissipation of its operations.