Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …

Inspired by biology, neuromorphic systems have been trying to emulate the human brain for
decades, taking advantage of its massive parallelism and sparse information coding …

Over the past few years, Spiking Neural Networks (SNNs) have become popular as a
possible pathway to enable low-power event-driven neuromorphic hardware. However, their …

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference
because the neurons in the networks are sparsely activated and computations are event …

Deep neural networks such as Convolutional Networks (ConvNets) and Deep Belief
Networks (DBNs) represent the state-of-the-art for many machine learning and computer …

The increasing demand for on-chip edge intelligence has motivated the exploration of
algorithmic techniques and specialized hardware to reduce the computing energy of current …

Deep Belief Networks (DBNs) have recently shown impressive performance on a broad
range of classification problems. Their generative properties allow better understanding of …

Low-latency deep spiking neural networks (SNNs) have become a promising alternative to
conventional artificial neural networks (ANNs) because of their potential for increased …

Historically, progress in neural networks and deep learning research has been greatly
influenced by the available hardware and software tools. This paper identifies trends in deep …

Spiking neural networks (SNNs) are potentially highly efficient models for inference on fully
parallel neuromorphic hardware, but existing training methods that convert conventional …