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 …
Evolutionary spiking neural networks: a survey
S Shen, R Zhang, C Wang, R Huang… - Journal of Membrane …, 2024 - Springer
Spiking neural networks (SNNs) are gaining increasing attention as potential
computationally efficient alternatives to traditional artificial neural networks (ANNs) …
computationally efficient alternatives to traditional artificial neural networks (ANNs) …
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 …
Scalable MatMul-free Language Modeling
Matrix multiplication (MatMul) typically dominates the overall computational cost of large
language models (LLMs). This cost only grows as LLMs scale to larger embedding …
language models (LLMs). This cost only grows as LLMs scale to larger embedding …
Ternary Spike-based Neuromorphic Signal Processing System
S Wang, D Zhang, A Belatreche, Y Xiao, H Qing… - arXiv preprint arXiv …, 2024 - arxiv.org
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 …
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 …
Autonomous Driving with Spiking Neural Networks
Autonomous driving demands an integrated approach that encompasses perception,
prediction, and planning, all while operating under strict energy constraints to enhance …
prediction, and planning, all while operating under strict energy constraints to enhance …
SpikeVoice: High-Quality Text-to-Speech Via Efficient Spiking Neural Network
Brain-inspired Spiking Neural Network (SNN) has demonstrated its effectiveness and
efficiency in vision, natural language, and speech understanding tasks, indicating their …
efficiency in vision, natural language, and speech understanding tasks, indicating their …
SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms
Towards energy-efficient artificial intelligence similar to the human brain, the bio-inspired
spiking neural networks (SNNs) have advantages of biological plausibility, event-driven …
spiking neural networks (SNNs) have advantages of biological plausibility, event-driven …
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 …