Optimized potential initialization for low-latency spiking neural networks
Abstract Spiking Neural Networks (SNNs) have been attached great importance due to the
distinctive properties of low power consumption, biological plausibility, and adversarial …
distinctive properties of low power consumption, biological plausibility, and adversarial …
Optimal ann-snn conversion for fast and accurate inference in deep spiking neural networks
Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have
attracted great attentions from researchers and industry. The most efficient way to train deep …
attracted great attentions from researchers and industry. The most efficient way to train deep …
Optimal ANN-SNN conversion for high-accuracy and ultra-low-latency spiking neural networks
Spiking Neural Networks (SNNs) have gained great attraction due to their distinctive
properties of low power consumption and fast inference on neuromorphic hardware. As the …
properties of low power consumption and fast inference on neuromorphic hardware. As the …
Bridging the gap between anns and snns by calibrating offset spikes
Spiking Neural Networks (SNNs) have attracted great attention due to their distinctive
characteristics of low power consumption and temporal information processing. ANN-SNN …
characteristics of low power consumption and temporal information processing. ANN-SNN …
Reducing ann-snn conversion error through residual membrane potential
Abstract Spiking Neural Networks (SNNs) have received extensive academic attention due
to the unique properties of low power consumption and high-speed computing on …
to the unique properties of low power consumption and high-speed computing on …
Optimal conversion of conventional artificial neural networks to spiking neural networks
Spiking neural networks (SNNs) are biology-inspired artificial neural networks (ANNs) that
comprise of spiking neurons to process asynchronous discrete signals. While more efficient …
comprise of spiking neurons to process asynchronous discrete signals. While more efficient …
Fast-SNN: fast spiking neural network by converting quantized ANN
Spiking neural networks (SNNs) have shown advantages in computation and energy
efficiency over traditional artificial neural networks (ANNs) thanks to their event-driven …
efficiency over traditional artificial neural networks (ANNs) thanks to their event-driven …
Spikeconverter: An efficient conversion framework zipping the gap between artificial neural networks and spiking neural networks
Abstract Spiking Neural Networks (SNNs) have recently attracted enormous research
interest since their event-driven and brain-inspired structure enables low-power …
interest since their event-driven and brain-inspired structure enables low-power …
Training high-performance low-latency spiking neural networks by differentiation on spike representation
Abstract Spiking Neural Network (SNN) is a promising energy-efficient AI model when
implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs …
implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs …
Revisiting batch normalization for training low-latency deep spiking neural networks from scratch
Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning
owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield …
owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield …