Spiking neural networks (SNNs) mimic brain computational strategies, and exhibit
substantial capabilities in spatiotemporal information processing. As an essential factor for …

Emergence of deep neural networks (DNNs) has raised enormous attention towards artificial
neural networks (ANNs) once again. They have become the state-of-the-art models and …

Deep neural networks have been successfully applied in the field of image recognition and
object detection, and the recognition results are close to or even superior to those from …

Human visual neurons rely on event-driven, energy-efficient spikes for communication, while
silicon image sensors do not. The energy-budget mismatch between biological systems and …

For embedded, mobile and edge-computing intelligent applications, this brief proposes a
low-cost real-time neuromorphic hardware system of spiking Extreme Learning Machine …

Convolutional neural networks (CNNs) are widely used in the field of fault diagnosis due to
their strong feature-extraction capability. However, in each timestep, CNNs only consider the …

This brief proposes an edge neuromorphic hardware design for real-time energy-efficient
applications. It is capable of fast on-chip learning on compressive sensed spikes utilizing an …

Abstract the remaining useful life (RUL) of rails is important to ensure safe and reliable
operation of railway transportation lines. However, the severity of deterioration of rails from …

Spiking neural network (SNN), as a brain-inspired model, possesses outstanding low power
consumption and the ability to mimic biological neuron mechanisms. Embodied vision is a …

Spiking neural networks (SNNs) have gained significant momentum in recent times as they
transmit information via discrete spikes, similar to neuromorphic low-power systems …