Spike-based neuromorphic hardware promises to reduce the energy consumption of image
classification and other deep-learning applications, particularly on mobile phones and other …

Spiking neural networks (SNNs) have shown advantages in computation and energy
efficiency over traditional artificial neural networks (ANNs) thanks to their event-driven …

Spiking and Quantized Neural Networks (NNs) are becoming exceedingly important for
hyper-efficient implementations of Deep Learning (DL) algorithms. However, these networks …

Biological neurons are known to have sparse and asynchronous communications using
spikes. Despite our incomplete understanding of processing strategies of the brain, its low …

Neuronflow is a neuromorphic, many core, data flow architecture that exploits brain-inspired
concepts to deliver a scalable event-based processing engine for neuron networks in Live AI …

Inference of Deep Neural Networks for stream signal (Video/Audio) processing in edge
devices is still challenging. Unlike the most state of the art inference engines which are …

Spiking Neural Networks (SNNs) can be configured to produce almost-equivalent accurate
Analog Neural Networks (ANNs) by various ANN-SNN conversion methods. Most of these …

Dynamical systems are universal computers. They can perceive stimuli, remember, learn
from feedback, plan sequences of actions, and coordinate complex behavioural responses …

Many efforts have been made to improve the neuron integration efficiency on neuromorphic
chips, such as using emerging memory devices and shrinking CMOS technology nodes …

Synaptic devices store the synaptic weight in spiking neural networks (SNNs). However,
because synaptic devices are based on memory cells, their synaptic weights are vulnerable …