Artificial neural networks (ANNs) have experienced a rapid advancement for their success in
various application domains, including autonomous driving and drone vision. Researchers …

Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental
theories, models, hardware architectures, and application systems toward more general …

Spiking neural networks (SNNs) have increasingly drawn massive research attention due to
biological interpretability and efficient computation. Recent achievements are devoted to …

Spiking Neural Network (SNN), the next generation of Neural Network, is supposed to be
more energy-saving than the previous generation represented by Convolution Neural …

Spiking Neural Networks (SNNs) are the next generation of Artificial Neural Networks
(ANNs) that utilize an event-based representation to perform more efficient computation …

Due to the ability to implement customized topology, FPGA is increasingly used to deploy
SNNs in both embedded and high-performance applications. In this paper, we survey state …

Abstract Spiking Neural Networks (SNNs) offer a promising alternative to traditional deep
learning frameworks, since they provide higher computational efficiency due to event-driven …

This paper presents a new methodology to alleviate the fundamental trade-off between
accuracy and latency in spiking neural networks (SNNs). The approach involves decoding …

As the third-generation neural networks, spiking neural networks (SNNs) have great
potential on neuromorphic hardware because of their high energy efficiency. However, deep …

Compared to conventional artificial neural networks, spiking neural networks (SNNs) are
more biologically plausible and require less computation due to their event-driven nature of …