Integer-valued training and spike-driven inference spiking neural network for high-performance and energy-efficient object detection
Abstract Brain-inspired Spiking Neural Networks (SNNs) have bio-plausibility and low-
power advantages over Artificial Neural Networks (ANNs). Applications of SNNs are …
power advantages over Artificial Neural Networks (ANNs). Applications of SNNs are …
Spikemba: Multi-modal spiking saliency mamba for temporal video grounding
Temporal video grounding (TVG) is a critical task in video content understanding, requiring
precise alignment between video content and natural language instructions. Despite …
precise alignment between video content and natural language instructions. Despite …
Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Networks
Neuromorphic object recognition with spiking neural networks (SNNs) is the cornerstone of
low-power neuromorphic computing. However, existing SNNs suffer from significant latency …
low-power neuromorphic computing. However, existing SNNs suffer from significant latency …
Knowing when to stop: Delay-adaptive spiking neural network classifiers with reliability guarantees
Spiking neural networks (SNNs) process time-series data via internal event-driven neural
dynamics. The energy consumption of an SNN depends on the number of spikes exchanged …
dynamics. The energy consumption of an SNN depends on the number of spikes exchanged …
Sharing leaky-integrate-and-fire neurons for memory-efficient spiking neural networks
Spiking Neural Networks (SNNs) have gained increasing attention as energy-efficient neural
networks owing to their binary and asynchronous computation. However, their non-linear …
networks owing to their binary and asynchronous computation. However, their non-linear …
Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training
The ambition of brain-inspired Spiking Neural Networks (SNNs) is to become a low-power
alternative to traditional Artificial Neural Networks (ANNs). This work addresses two major …
alternative to traditional Artificial Neural Networks (ANNs). This work addresses two major …
Are SNNs Truly Energy-efficient?—A Hardware Perspective
Spiking Neural Networks (SNNs) have gained attention for their energy-efficient machine
learning capabilities, utilizing bio-inspired activation functions and sparse binary spike-data …
learning capabilities, utilizing bio-inspired activation functions and sparse binary spike-data …
When in-memory computing meets spiking neural networks—A perspective on device-circuit-system-and-algorithm co-design
This review explores the intersection of bio-plausible artificial intelligence in the form of
spiking neural networks (SNNs) with the analog in-memory computing (IMC) domain …
spiking neural networks (SNNs) with the analog in-memory computing (IMC) domain …
Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks
Spiking neural networks (SNNs) are recurrent models that can leverage sparsity in input
time series to efficiently carry out tasks such as classification. Additional efficiency gains can …
time series to efficiently carry out tasks such as classification. Additional efficiency gains can …
Energy-Efficient Inference With Software-Hardware Co-Design for Sustainable Artificial Intelligence of Things
S Dai, Z Luo, W Luo, S Wang, C Dai… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
The emerging field of artificial intelligence of things (AIoT) is propelled by the remarkable
success of deep learning and hardware evolution, which has a significant impact on our …
success of deep learning and hardware evolution, which has a significant impact on our …