arXiv:2606.11236v1 Announce Type: cross Abstract: Training deep spiking neural networks (SNNs) remains challenging due to sharp loss landscapes and temporal inconsistency caused by surrogate gradients. To address these challenges, we propose a unified framework: adaptive and asymmetric surrogate gradients A2SG. The adaptive gradients adjust an effective window for spatio-temporal adaptation, reducing spatial gradient variation and maintaining directional consistency of gradients over time. The asymmetric gradients reflect neuronal dynamics by assigning larger gradients to neurons with higher m
The continuous research into more efficient and biologically plausible AI models, like Spiking Neural Networks (SNNs), is a constant effort to overcome the limitations of current deep learning paradigms.
Improved training methods for SNNs could lead to more energy-efficient and hardware-friendly AI, impacting edge computing and AI applications requiring low power consumption.
This research introduces a novel training framework that could make SNNs more practical and scalable for complex tasks, potentially broadening their application beyond neuromorphic hardware.
- · Neuromorphic computing hardware manufacturers
- · Edge AI providers
- · AI researchers working on energy efficiency
- · Developers solely focused on traditional ANNs for low-power applications (if SNN
More robust and efficient deep SNNs become viable for real-world applications.
Increased adoption of SNNs drives demand for specialized neuromorphic hardware and leads to new AI paradigms.
Widely available, low-power AI systems on edge devices could democratize advanced AI capabilities beyond cloud infrastructure.
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Read at arXiv cs.LG