Spiking neural networks for autonomous driving: A review
FS Martínez, J Casas-Roma, L Subirats… - … Applications of Artificial …, 2024 - Elsevier
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 …
Connection pruning for deep spiking neural networks with on-chip learning
Long training time hinders the potential of the deep, large-scale Spiking Neural Network
(SNN) with the on-chip learning capability to be realized on the embedded systems …
(SNN) with the on-chip learning capability to be realized on the embedded systems …
Reliable fleet analytics for edge iot solutions
E Raj, M Westerlund, L Espinosa-Leal - arXiv preprint arXiv:2101.04414, 2021 - arxiv.org
In recent years we have witnessed a boom in Internet of Things (IoT) device deployments,
which has resulted in big data and demand for low-latency communication. This shift in the …
which has resulted in big data and demand for low-latency communication. This shift in the …
ANAS: Asynchronous Neuromorphic Hardware Architecture Search Based on a System-Level Simulator
Event-driven asynchronous neuromorphic hardware is emerging for edge computing with
high energy efficiency. In order to obtain the architecture with the best hardware …
high energy efficiency. In order to obtain the architecture with the best hardware …
Natural Language to Verilog: Design of a Recurrent Spiking Neural Network using Large Language Models and ChatGPT
P Vitolo, G Psaltakis, M Tomlinson… - arXiv preprint arXiv …, 2024 - arxiv.org
This paper investigates the use of Large Language Models (LLMs) for automating the
generation of hardware description code, aiming to explore their potential in supporting and …
generation of hardware description code, aiming to explore their potential in supporting and …