24 citations · 58 across the 15 of their papers we have counts for
3 papers · 1 filter
DeepFire2: A Convolutional Spiking Neural Network Accelerator on FPGAs
Myat Thu Linn Aung, Daniel Gerlinghoff, Chuping Qu +5
Brain-inspired spiking neural networks (SNNs) replace the multiply-accumulate operations of traditional neural networks by integrate-and-fire neurons, with the goal of achieving gr…
Desire Backpropagation: A Lightweight Training Algorithm for Multi-Layer Spiking Neural Networks based on Spike-Timing-Dependent Plasticity
Daniel Gerlinghoff, Tao Luo, Rick Siow Mong Goh +1
Spiking neural networks (SNNs) are a viable alternative to conventional artificial neural networks when resource efficiency and computational complexity are of importance. A major…
Low Latency Conversion of Artificial Neural Network Models to Rate-encoded Spiking Neural Networks
Zhanglu Yan, Jun Zhou, Weng-Fai Wong
Spiking neural networks (SNNs) are well suited for resource-constrained applications as they do not need expensive multipliers. In a typical rate-encoded SNN, a series of binary sp…