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Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware
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 …
attention lately due to its promise of reducing the computational energy, latency, as well as …
A survey of encoding techniques for signal processing in spiking neural networks
Biologically inspired spiking neural networks are increasingly popular in the field of artificial
intelligence due to their ability to solve complex problems while being power efficient. They …
intelligence due to their ability to solve complex problems while being power efficient. They …
Advancing neuromorphic computing with loihi: A survey of results and outlook
Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
SpikedAttention: Training-Free and Fully Spike-Driven Transformer-to-SNN Conversion with Winner-Oriented Spike Shift for Softmax Operation
Event-driven spiking neural networks (SNNs) are promising neural networks that reduce the
energy consumption of continuously growing AI models. Recently, kee** pace with the …
energy consumption of continuously growing AI models. Recently, kee** pace with the …
Spiking neural networks for biomedical signal analysis
Artificial intelligence (AI) has had a significant impact on human life because of its
pervasiveness across industries and its rapid development. Although AI has achieved …
pervasiveness across industries and its rapid development. Although AI has achieved …
One-spike snn: Single-spike phase coding with base manipulation for ann-to-snn conversion loss minimization
As spiking neural networks (SNNs) are event-driven, energy efficiency is higher than
conventional artificial neural networks (ANNs). Since SNN delivers data through discrete …
conventional artificial neural networks (ANNs). Since SNN delivers data through discrete …
Lite-snn: Leveraging inherent dynamics to train energy-efficient spiking neural networks for sequential learning
Spiking neural networks (SNNs) are gaining popularity for their promise of low-power
machine intelligence on event-driven neuromorphic hardware. SNNs have achieved …
machine intelligence on event-driven neuromorphic hardware. SNNs have achieved …
Cade: Cosine annealing differential evolution for spiking neural network
Spiking neural networks (SNNs) have gained prominence for their potential in neuromorphic
computing and energy-efficient artificial intelligence, yet optimizing them remains a …
computing and energy-efficient artificial intelligence, yet optimizing them remains a …
Temporal pattern coding in deep spiking neural networks
Deep Artificial Neural Networks (ANNs) employ a simplified analog neuron model that
mimics the rate transfer function of integrate-and-fire neurons. In Spiking Neural Networks …
mimics the rate transfer function of integrate-and-fire neurons. In Spiking Neural Networks …
Efficient Implementation of Spiking Neural Networks for Inference using Ex-Situ Training
This paper introduces a novel method for designing and simulating neuromorphic circuits for
inference tasks, utilizing spiking neural networks (SNNs) trained ex-situ to offer practical …
inference tasks, utilizing spiking neural networks (SNNs) trained ex-situ to offer practical …