Gaining the Sparse Rewards by Exploring Lottery Tickets in Spiking Neural Network

arXiv:2309.13302v3 Announce Type: replace-cross
Abstract: Deploying energy-efficient deep learning algorithms on computational-limited devices, such as robots, is still a pressing issue for real-world applications. Spiking Neural Networks (SNNs), a novel brain-inspired algorithm, offer a promising solution due to their low-latency and low-energy properties over traditional Artificial Neural Networks (ANNs). Despite their advantages, the dense structure of deep SNNs can still result in extra energy consumption. The Lottery Ticket Hypothesis (LTH) posits that within dense neural networks, there exist winning Lottery …

abstract algorithm algorithms applications artificial artificial neural networks arxiv brain brain-inspired computational cs.cv cs.ne deep learning deep learning algorithms devices energy issue latency low low-energy network networks neural network neural networks novel robots solution spiking neural network spiking neural networks tickets type world