Direct training high-performance deep spiking neural networks: a review of theories and methods
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial
neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …
neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …
Brain-inspired computing: A systematic survey and future trends
Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental
theories, models, hardware architectures, and application systems toward more general …
theories, models, hardware architectures, and application systems toward more general …
Integer-valued training and spike-driven inference spiking neural network for high-performance and energy-efficient object detection
Abstract Brain-inspired Spiking Neural Networks (SNNs) have bio-plausibility and low-
power advantages over Artificial Neural Networks (ANNs). Applications of SNNs are …
power advantages over Artificial Neural Networks (ANNs). Applications of SNNs are …
Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directions
Deep learning has revolutionized artificial intelligence (AI), achieving remarkable progress
in fields such as computer vision, speech recognition, and natural language processing …
in fields such as computer vision, speech recognition, and natural language processing …
Scaling spike-driven transformer with efficient spike firing approximation training
The ambition of brain-inspired Spiking Neural Networks (SNNs) is to become a low-power
alternative to traditional Artificial Neural Networks (ANNs). This work addresses two major …
alternative to traditional Artificial Neural Networks (ANNs). This work addresses two major …
MetaLA: Unified optimal linear approximation to softmax attention map
Various linear complexity models, such as Linear Transformer (LinFormer), State Space
Model (SSM), and Linear RNN (LinRNN), have been proposed to replace the conventional …
Model (SSM), and Linear RNN (LinRNN), have been proposed to replace the conventional …
RSC-SNN: Exploring the Trade-off Between Adversarial Robustness and Accuracy in Spiking Neural Networks via Randomized Smoothing Coding
Spiking Neural Networks (SNNs) have received widespread attention due to their unique
neuronal dynamics and low-power nature. Previous research empirically shows that SNNs …
neuronal dynamics and low-power nature. Previous research empirically shows that SNNs …
BKDSNN: Enhancing the Performance of Learning-Based Spiking Neural Networks Training with Blurred Knowledge Distillation
Spiking neural networks (SNNs), which mimic biological neural systems to convey
information via discrete spikes, are well-known as brain-inspired models with excellent …
information via discrete spikes, are well-known as brain-inspired models with excellent …
Ternary spike-based neuromorphic signal processing system
Deep Neural Networks (DNNs) have been successfully implemented across various signal
processing fields, resulting in significant enhancements in performance. However, DNNs …
processing fields, resulting in significant enhancements in performance. However, DNNs …
SVFormer: a direct training spiking transformer for efficient video action recognition
Video action recognition (VAR) plays crucial roles in various domains such as surveillance,
healthcare, and industrial automation, making it highly significant for the society …
healthcare, and industrial automation, making it highly significant for the society …