Spiking Neural Networks (SNNs) is a promising paradigm for efficient event-driven
processing of spatio-temporally sparse data streams. Spiking Neural Networks (SNNs) have …

Bio-inspired computing architecture based on artificial neurons and synapses is attracting
intensive attention in the field of power-efficient artificial intelligence. Artificial neurons with …

Neuromorphic processors aim to emulate the biological principles of the brain to achieve
high efficiency with low power consumption. However, the lack of flexibility in most …

We propose a sparse computation method for optimizing the inference of neural networks in
reinforcement learning (RL) tasks. Motivated by the processing abilities of the brain, this …

Spiking neural networks (SNNs) compute in an event-based manner to achieve a more
efficient computation than standard neural networks. In SNNs, neuronal outputs are not …

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 …

Non-intrusive visual-based applications supporting the communication of people employing
sign language for communication are always an open and attractive research field for the …

Sparse and event-driven spiking neural network (SNN) algorithms are the ideal candidate
solution for energy-efficient edge computing. Yet, with the growing complexity of SNN …

Reducing energy consumption is a critical point for neural network models running on edge
devices. In this regard, reducing the number of multiply-accumulate (MAC) operations of …