TL;DR
MPL-schedule is proposed, a multi-preemptive, priority-aware scheduling scheme that dynamically coordinates heterogeneous SNN tasks on GPUs that improves throughput by up to 15.0% and energy efficiency by up to 20.3%, while sustaining over 95.8% GPU utilization.
Extensive experiments on static and neuromorphic benchmarks show that lower-bit BASC models match or outperform higher-bit baselines and retain this accuracy advantage after structured pruning, while further reducing model storage and synaptic operations.
Spiking Neural Networks (SNNs) provide a natural computation model for neuromorphic hardware, but fixed-timestep inference can execute substantial redundant temporal computation. This work proposes a hardware-aware early termination (ET) framework that determines the stopping time from accumulated output-spike statisti...
Spiking forecasting framework SpikeLite is introduced, a spiking forecasting framework built around two modules: a Frequency-Selective Spiking Encoder for frequency-sensitive temporal encoding and a Sparse Spiking Channel Attention (SSCA) module for selective cross-channel interaction.
Spiking neural networks are attractive for low-power speech command recognition, yet their latency has received far less attention than their energy efficiency, and their multi-timestep execution is widely assumed to make them slower than quantized neural networks. This paper challenges the assumption that more local t...
Spiking Neural Networks (SNNs) offer a promising path toward ultra-low-power artificial intelligence inference by emulating the event-driven computation of biological neurons. However, two challenges limit their practical deployment. First, fixed-parameter Leaky Integrate-and-Fire (LIF) neurons lack the adaptation mech...
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