2 citations · 2 across the 14 of their papers we have counts for
15 papers
Latency and accuracy tradeoffs in Spiking Neural Networks
Zhanglu Yan, Zixuan Zhu, Kaiwen Tang +3
Spiking neural networks are attractive for low-power speech command recognition, yet their latency has received far less attention than their energy efficiency, and their multi-tim…
Lapis: Laplacian Spiking Attention via First-Spike Timing and Membrane Leakage
Kaiwen Tang, Jiaqi Zheng, Zixuan Zhu +3
Self-attention has become central to spiking vision transformers, yet its query-key scoring is still largely inherited from dense networks. Existing spiking variants either simplif…
Efficient and Robust Spiking Neural Networks for sEMG-Based Muscle Fatigue Detection
Kaiwen Tang, Jiaqi Dong, Zhanglu Yan +1
Detecting muscle fatigue via surface electromyography (sEMG) is essential for applications in sports, rehabilitation, and wearable health monitoring. Accurate and timely detection…
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.…
ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization
Kaiwen Tang, Di Yu, Jiaqi Zheng +4
Spiking neural networks (SNNs) are promising for edge sensing due to their event-driven computation and temporal filtering capability. However, standard leaky integrate-and-fire (L…
SpikySpace: A Spiking State Space Model for Energy-Efficient Time Series Forecasting
Kaiwen Tang, Jiaqi Zheng, Yuze Jin +4
Time-series forecasting in domains like traffic management and industrial monitoring often requires real-time, energy-efficient processing on edge devices with limited resources. S…
Kirin: Improving ANN efficiency with SNN Hybridization
Chenyu Wang, Zhanglu Yan, Zhi Zhou +2
Artificial neural networks (ANNs), particularly large language models (LLMs), demonstrate powerful inference capabilities but consume substantial energy. Conversely, spiking neural…
Matterhorn: Masked Time-to-First-Spike Encoding by Reassigning the Silent State for Sparse and Energy-Efficient Spiking Transformers
Zhanglu Yan, Kaiwen Tang, Zixuan Zhu +4
Spiking neural networks (SNNs) promise energy-efficient inference for large language models (LLMs), yet most reported savings rely on compute-operation counts that overlook data mo…
Energy-Efficient and Dequantization-Free Q-LLMs: A Spiking Neural Network Approach to Salient Value Mitigation
Chenyu Wang, Zhanglu Yan, Zhi Zhou +2
In the era of large language models (LLMs), weight-activation quantization helps fit models on edge device by reducing memory and compute bit-widths. However, three challenges pers…
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…
Optimizing Neural Networks with Learnable Non-Linear Activation Functions via Lookup-Based FPGA Acceleration
Mengyuan Yin, Benjamin Chen Ming Choong, Chuping Qu +3
Learned activation functions in models like Kolmogorov-Arnold Networks (KANs) outperform fixed-activation architectures in terms of accuracy and interpretability; however, their co…
Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model
Kaiwen Tang, Zhanglu Yan, Weng-Fai Wong
For reasons such as privacy, there are use cases for language models at the edge. This has given rise to small language models targeted for deployment in resource-constrained devic…
Reconsidering the Energy Efficiency of Spiking Neural Networks Inference from Analytical Perspectives
Zhanglu Yan, Zhenyu Bai, Kaiwen Tang +1
Spiking Neural Networks (SNNs) promise higher energy efficiency over conventional Quantized Artificial Neural Networks (QNNs) due to their event-driven, spike-based computation. Ho…
SparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classification
Zhanglu Yan, Zhenyu Bai, Tulika Mitra +1
Deep learning has driven significant technological advancements, but its high energy consumption limits its use on battery-operated edge devices. Spiking Neural Networks (SNNs) off…
Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-terminal Coding Sequences
Zhanglu Yan, Weiran Chu, Yuhua Sheng +4
N-terminal coding sequence (NCS) influences gene expression by impacting the translation initiation rate. The NCS optimization problem is to find an NCS that maximizes gene express…