8 citations · 13 across the 4 of their papers we have counts for
4 papers
Otters: An Energy-Efficient SpikingTransformer via Optical Time-to-First-Spike Encoding
Zhanglu Yan, Jiayi Mao, Qianhui Liu +5
Spiking neural networks (SNNs) promise high energy efficiency, particularly with time-to-first-spike (TTFS) encoding, which maximizes sparsity by emitting at most one spike per neu…
Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems
Benjamin Chen Ming Choong, Tao Luo, Cheng Liu +3
Deep neural networks generate and process large volumes of data, posing challenges for low-resource embedded systems. In-memory computing has been demonstrated as an efficient comp…
A Resource-efficient Spiking Neural Network Accelerator Supporting Emerging Neural Encoding
Daniel Gerlinghoff, Zhehui Wang, Xiaozhe Gu +2
Spiking neural networks (SNNs) recently gained momentum due to their low-power multiplication-free computing and the closer resemblance of biological processes in the nervous syste…
Optimizing for In-memory Deep Learning with Emerging Memory Technology
Zhehui Wang, Tao Luo, Rick Siow Mong Goh +2
In-memory deep learning computes neural network models where they are stored, thus avoiding long distance communication between memory and computation units, resulting in considera…