SNN Based

Spiking Neural Networks (SNNs), inspired by the brain's biological neural networks, aim to create energy-efficient artificial intelligence by processing information via discrete spikes. Current research focuses on improving SNN performance and training efficiency through hybrid SNN-ANN architectures, novel neuron models (e.g., Complementary Leaky Integrate-and-Fire), advanced training methods like knowledge distillation and contrastive learning, and optimized hardware implementations for faster training and inference. These advancements are significant because they address the limitations of traditional ANNs in terms of energy consumption and pave the way for deploying AI in resource-constrained environments, particularly for applications like real-time object detection and industrial fault diagnosis.

Papers

November 1, 2024

Differentiable architecture search with multi-dimensional attention for spiking neural networks
Yilei Man, Linhai Xie, Shushan Qiao, Yumei Zhou, Delong Shang
Neural Network Neural Architecture Search Spiking Neural Network Differentiable Architecture Search Dimensional Attention SNN Based

September 3, 2024

ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action Recognition
Shiting Xiao, Yuhang Li, Youngeun Kim, Donghyun Lee, Priyadarshini Panda
Action Recognition Spiking Neural Network Neural Network Baseline Hybrid Framework SNN Based

August 17, 2024

Toward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks
Yongqi Ding, Lin Zuo, Mengmeng Jing, Kunshan Yang, Biao Chen, Yunqian Yu
End to End Spiking Neural Network Multi Scale SNN Based Industrial Scenario

August 1, 2024

Towards Scalable GPU-Accelerated SNN Training via Temporal Fusion
Yanchen Li, Jiachun Li, Kebin Sun, Luziwei Leng, Ran Cheng
Potential Scalability Temporal Fusion SNN Model SNN Based Neural Network Library

July 12, 2024

BKDSNN: Enhancing the Performance of Learning-based Spiking Neural Networks Training with Blurred Knowledge Distillation
Zekai Xu, Kang You, Qinghai Guo, Xiang Wang, Zhezhi He
Knowledge Distillation System Performance Spiking Neural Network Neuromorphic Datasets SNN Model C2p GCN SNN Based

June 11, 2024

SpikePipe: Accelerated Training of Spiking Neural Networks via Inter-Layer Pipelining and Multiprocessor Scheduling
Sai Sanjeet, Bibhu Datta Sahoo, Keshab K. Parhi
Spiking Neural Network Back Propagation Faster Training Heterogeneous Computing Hardware Acceleration SNN Based

March 15, 2024

A Hybrid SNN-ANN Network for Event-based Object Detection with Spatial and Temporal Attention
Soikat Hasan Ahmed, Jan Finkbeiner, Emre Neftci
Neuromorphic Hardware Temporal Attention Spatial Domain Attention Based Deep Hybrid Neural Network Attention Based Network SNN Based Event Based Object Detection

February 7, 2024

CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks
Yulong Huang, Xiaopeng Lin, Hongwei Ren, Haotian Fu, Yue Zhou, Zunchang Liu, Biao Pan, Bojun Cheng
Neural Network Neuron Model Energy Efficient SNN Based Leaky Integrate and Fire Leaky Integrate and Fire Neuron

November 24, 2023

Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks
Ziqing Wang, Yuetong Fang, Jiahang Cao, Renjing Xu
Spiking Neural Network Neuron Model Unifying Framework SNN Based Spike Stream Adaptive Calibration

November 3, 2023

Enhancing Functional Data Analysis with Sequential Neural Networks: Advantages and Comparative Study
J. Zhao, J. Li, M. Chen, S. Jadhav
Comparative Study Advantage Feedback Functional Data Analysis SNN Based SNN Architecture Sequential Interaction Network

August 9, 2023

Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks
Jue Chen, Huan Yuan, Jianchao Tan, Bin Chen, Chengru Song, Di Zhang
Neural Network Model Compression Minimax Optimization Neuromorphic Chip SNN Based

June 3, 2023

Transforming to Yoked Neural Networks to Improve ANN Structure
Xinshun Liu, Yizhi Fang, Yichao Jiang
Character Transformation Classical Neural Network Traditional Neural Network SNN Based Neural Network Structure Deep Learning Module

May 23, 2023

Temporal Contrastive Learning for Spiking Neural Networks
Haonan Qiu, Zeyin Song, Yanqi Chen, Munan Ning, Wei Fang, Tao Sun, Zhengyu Ma, Li Yuan, Yonghong Tian
Neural Network Contrastive Learning Temporal Correlation SNN Based Temporal Domain

April 12, 2023

Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation
Qi Xu, Yaxin Li, Jiangrong Shen, Jian K Liu, Huajin Tang, Gang Pan
Neural Network Knowledge Distillation Spike Train SNN Based Brain Inspired Computing Deep Spiking Neural Network Student GNN

June 8, 2022

Construction of a spike-based memory using neural-like logic gates based on Spiking Neural Networks on SpiNNaker
Alvaro Ayuso-Martinez, Daniel Casanueva-Morato, Juan P. Dominguez-Morales, Angel Jimenez-Fernandez, Gabriel Jimenez-Moreno
Spiking Neural Network Construction Industry Neuromorphic Engineering SNN Based Neuromorphic Platform

May 24, 2022

lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Autonomous Agent SNN Based Unsupervised Continual Learning

May 17, 2022

Spiking Neural Operators for Scientific Machine Learning
Adar Kahana, Qian Zhang, Leonard Gleyzer, George Em Karniadakis
Physic Informed Neural Network Scientific Machine Learning Fully Connected Neural Network SNN Based Regression Function

January 26, 2022

Meta-learning Spiking Neural Networks with Surrogate Gradient Descent
Kenneth Stewart, Emre Neftci
Spiking Neural Network Neuromorphic Hardware Gradient Based Meta Learning SNN Based Surrogate Gradient Neuromorphic Learning

SNN Based

Papers

Differentiable architecture search with multi-dimensional attention for spiking neural networks

ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action Recognition

Toward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks

Towards Scalable GPU-Accelerated SNN Training via Temporal Fusion

BKDSNN: Enhancing the Performance of Learning-based Spiking Neural Networks Training with Blurred Knowledge Distillation

SpikePipe: Accelerated Training of Spiking Neural Networks via Inter-Layer Pipelining and Multiprocessor Scheduling

A Hybrid SNN-ANN Network for Event-based Object Detection with Spatial and Temporal Attention

CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks

Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks

Enhancing Functional Data Analysis with Sequential Neural Networks: Advantages and Comparative Study

Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks

Transforming to Yoked Neural Networks to Improve ANN Structure

Temporal Contrastive Learning for Spiking Neural Networks

Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation

Construction of a spike-based memory using neural-like logic gates based on Spiking Neural Networks on SpiNNaker

lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents

Spiking Neural Operators for Scientific Machine Learning

Meta-learning Spiking Neural Networks with Surrogate Gradient Descent