8 citations · 9 across the 8 of their papers we have counts for
10 papers
Load-Balanced Neural-Network-Aided Adaptive Tabulation of Phase Equilibria for Transcritical Multiphase Flows
Navneeth Srinivasan, Po-Yu Lai, Suo Yang
Simulating high-pressure transcritical flows with phase change is critical for advanced propulsion, yet traditional first-principles approaches, vapor-liquid equilibrium (VLE) calc…
A Time-Variant Spiking Neuron from a Unified State-Dynamics Perspective
Jing Yang, Qianhui Liu, Gang Pan +1
Spiking neural networks (SNNs) have increasingly incorporated rich neuronal dynamics to enhance temporal processing. However, richer dynamics do not necessarily enable neurons to a…
Benchmarking spiking neural networks across sensing modalities on edge devices
Xin Du, Di Yu, Changze Lv +12
Edge computing systems need to support diverse sensing workloads under tight energy and memory constraints, thereby motivating deployment-aware model selection. Spiking neural netw…
Otters++: A Time-to-first-spike Based Energy Efficient Optical Spiking Transformer
Zhanglu Yan, Jiayi Mao, Kaiwen Tang +6
Spiking neural networks (SNNs) are promising for energy-efficient inference, and time-to-first-spike (TTFS) coding is especially attractive because each neuron fires at most once.…
UniSpike: Accelerating Spiking Neural Networks on Neuromorphic Systems via Eliminating Address Redundancy
Qinghui Xing, Zhuo Chen, Xin Du +6
Many-core neuromorphic systems accelerate Spiking Neural Networks (SNNs), yet their packet-based spike communication can spend substantial traffic and energy repeatedly transmittin…
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…
TS-SNN: Temporal Shift Module for Spiking Neural Networks
Kairong Yu, Tianqing Zhang, Qi Xu +2
Spiking Neural Networks (SNNs) are increasingly recognized for their biological plausibility and energy efficiency, positioning them as strong alternatives to Artificial Neural Net…
Human-Inspired Computing for Robust and Efficient Audio-Visual Speech Recognition
Qianhui Liu, Jiadong Wang, Yang Wang +3
Humans naturally perform audiovisual speech recognition (AVSR), enhancing the accuracy and robustness by integrating auditory and visual information. Spiking neural networks (SNNs)…
Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning
Yaxin Li, Qi Xu, Jiangrong Shen +3
The emergence of deep and large-scale spiking neural networks (SNNs) exhibiting high performance across diverse complex datasets has led to a need for compressing network models du…
LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
Qianhui Liu, Jiaqi Yan, Malu Zhang +2
Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient, making them well-suited for low-power edge devices. H…