Backpropagation-based learning techniques for deep spiking neural networks: A survey
M Dampfhoffer, T Mesquida… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the adoption of smart systems, artificial neural networks (ANNs) have become
ubiquitous. Conventional ANN implementations have high energy consumption, limiting …
ubiquitous. Conventional ANN implementations have high energy consumption, limiting …
Federated learning-empowered mobile network management for 5G and beyond networks: From access to core
The fifth generation (5G) and beyond wireless networks are envisioned to provide an
integrated communication and computing platform that will enable multipurpose and …
integrated communication and computing platform that will enable multipurpose and …
Learning rules in spiking neural networks: A survey
Spiking neural networks (SNNs) are a promising energy-efficient alternative to artificial
neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal …
neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal …
Snn-rat: Robustness-enhanced spiking neural network through regularized adversarial training
Spiking neural networks (SNNs) are promising to be widely deployed in real-time and safety-
critical applications with the advance of neuromorphic computing. Recent work has …
critical applications with the advance of neuromorphic computing. Recent work has …
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 …
Spiking neural networks for autonomous driving: A review
The rapid progress of autonomous driving (AD) has triggered a surge in demand for safer
and more efficient autonomous vehicles, owing to the intricacy of modern urban …
and more efficient autonomous vehicles, owing to the intricacy of modern urban …
VTSNN: a virtual temporal spiking neural network
Spiking neural networks (SNNs) have recently demonstrated outstanding performance in a
variety of high-level tasks, such as image classification. However, advancements in the field …
variety of high-level tasks, such as image classification. However, advancements in the field …
Neurobench: Advancing neuromorphic computing through collaborative, fair and representative benchmarking
The field of neuromorphic computing holds great promise in terms of advancing computing
efficiency and capabilities by following brain-inspired principles. However, the rich diversity …
efficiency and capabilities by following brain-inspired principles. However, the rich diversity …
Enhancing the robustness of spiking neural networks with stochastic gating mechanisms
Spiking neural networks (SNNs) exploit neural spikes to provide solutions for low-power
intelligent applications on neuromorphic hardware. Although SNNs have high computational …
intelligent applications on neuromorphic hardware. Although SNNs have high computational …
Spikesim: An end-to-end compute-in-memory hardware evaluation tool for benchmarking spiking neural networks
Spiking neural networks (SNNs) are an active research domain toward energy-efficient
machine intelligence. Compared to conventional artificial neural networks (ANNs), SNNs …
machine intelligence. Compared to conventional artificial neural networks (ANNs), SNNs …