Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …

Abstract Spiking Neural Networks (SNNs) have attracted enormous research interest due to
temporal information processing capability, low power consumption, and high biological …

The field of neuromorphic computing promises extremely low-power and low-latency
sensing and processing. Challenges in transferring learning algorithms from traditional …

Neuromorphic vision sensor is a new bio-inspired imaging paradigm that reports
asynchronous, continuously per-pixel brightness changes called'events' with high temporal …

Event-based cameras have attracted increasing attention due to their advantages of
biologically inspired paradigm and low power consumption. Since event-based cameras …

The main streams of human activity recognition (HAR) algorithms are developed based on
RGB cameras which usually suffer from illumination, fast motion, privacy preservation, and …

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 …

Event cameras are bio-inspired sensors that respond to local changes in light intensity and
feature low latency, high energy efficiency, and high dynamic range. Meanwhile, Spiking …

Event camera-based pattern recognition is a newly arising research topic in recent years.
Current researchers usually transform the event streams into images, graphs, or voxels, and …

Implementing neural networks (NN) on edge devices enables AI to be applied in many daily
scenarios. The stringent area and power budget on edge devices impose challenges on …