Spiking Neural Network
Spiking neural networks (SNNs), inspired by the brain's event-driven communication, aim to create energy-efficient artificial intelligence by processing information through binary spikes rather than continuous values. Current research emphasizes improving training efficiency through novel neuron models (e.g., parallel resonate and fire neurons, multi-compartment neurons), developing specialized weight initialization methods, and exploring various coding schemes (e.g., Poisson coding, stepwise weighted spike coding) to optimize performance and reduce energy consumption. This field is significant due to SNNs' potential for low-power applications in embedded systems, neuromorphic computing, and real-time signal processing tasks like robotic manipulation and brain-computer interfaces.
Papers
Self-Attentive Spatio-Temporal Calibration for Precise Intermediate Layer Matching in ANN-to-SNN Distillation
Di Hong, Yueming Wang
Spiking Neural Network Accelerator Architecture for Differential-Time Representation using Learned Encoding
Daniel Windhager, Lothar Ratschbacher, Bernhard A. Moser, Michael Lunglmayr
Enhanced Temporal Processing in Spiking Neural Networks for Static Object Detection Using 3D Convolutions
Huaxu He
HPCNeuroNet: A Neuromorphic Approach Merging SNN Temporal Dynamics with Transformer Attention for FPGA-based Particle Physics
Murat Isik, Hiruna Vishwamith, Jonathan Naoukin, I. Can Dikmen
FedLEC: Effective Federated Learning Algorithm with Spiking Neural Networks Under Label Skews
Di Yu, Xin Du, Linshan Jiang, Shunwen Bai, Wentao Tong, Shuiguang Deng
Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Network
Ziqing Wang, Yuetong Fang, Jiahang Cao, Hongwei Ren, Renjing Xu
Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation
Zecheng Hao, Zhaofei Yu, Tiejun Huang
Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks
Hangming Zhang, Alexander Sboev, Roman Rybka, Qiang Yu
Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation
Jiaqi Wang, Liutao Yu, Liwei Huang, Chenlin Zhou, Han Zhang, Zhenxi Song, Min Zhang, Zhengyu Ma, Zhiguo Zhang
Efficient Event-based Semantic Segmentation with Spike-driven Lightweight Transformer-based Networks
Xiaxin Zhu, Fangming Guo, Xianlei Long, Qingyi Gu, Chao Chen, Fuqiang Gu