40 citations · 79 across the 6 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…
E3NE: An End-to-End Framework for Accelerating Spiking Neural Networks with Emerging Neural Encoding on FPGAs
Daniel Gerlinghoff, Zhehui Wang, Xiaozhe Gu +2
Compiler frameworks are crucial for the widespread use of FPGA-based deep learning accelerators. They allow researchers and developers, who are not familiar with hardware engineeri…