Advancing Direct Training for Spiking Neural Networks with Circulate-Firing Neurons and Learnable Gradients
arXiv:2605.27412v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) have emerged with promising energy-efficient property, yet a substantial performance gap persists compared to Artificial Neural Networks (ANNs). This gap stems from at least two key limitations: first, conventional spiking neurons offer limited information representation capacity, underutilizing the rich dynamics of membrane potentials; second, fixed surrogate gradient (SG) functions across time steps leads to imprecise gradient propagation, impeding effective direct training. To address these two challenges, we p
The continuous drive for more energy-efficient and powerful AI models, particularly for on-device and embedded applications, necessitates advancements in architectures like Spiking Neural Networks.
Improving the performance and training efficiency of SNNs could unlock new frontiers in AI hardware and sustainable AI practices, bridging the gap with traditional ANNs while consuming less power.
Direct training methods and novel neuron designs for SNNs could make them a more viable and competitive alternative to ANNs, particularly where energy consumption is a critical factor.
- · AI hardware developers
- · Edge computing industries
- · IoT device manufacturers
- · Sustainable AI initiatives
- · Developers solely focused on traditional ANNs
More powerful and efficient SNNs could accelerate the adoption of AI in energy-constrained environments.
Increased SNN viability might shift investment and research focus towards more biologically inspired computing architectures.
A significant leap in SNN efficiency could enable widespread ubiquitous AI, integrated deeply into our physical world with minimal power draw.
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Read at arXiv cs.LG