24 citations · 53 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
ReGraph: Scaling Graph Processing on HBM-enabled FPGAs with Heterogeneous Pipelines
Xinyu Chen, Yao Chen, Feng Cheng +3
The use of FPGAs for efficient graph processing has attracted significant interest. Recent memory subsystem upgrades including the introduction of HBM in FPGAs promise to further a…
Benchmarking Quantum(-inspired) Annealing Hardware on Practical Use Cases
Tian Huang, Jun Xu, Tao Luo +3
Quantum(-inspired) annealers show promise in solving combinatorial optimisation problems in practice. There has been extensive researches demonstrating the utility of D-Wave quantu…