aiqbench analysis
This research introduces SpiKFAX, a second-order optimization method tailored for Spiking Neural Networks (SNNs) to overcome the training challenges posed by their discrete and sparse dynamics. By utilizing a Kronecker-factored approximation of the Fisher information matrix, the approach improves test accuracy and stability across multiple architectures compared to standard optimizers like Adam. The work addresses a specific bottleneck in energy-efficient neural computing, offering a more robust mathematical framework for advancing SNN deployment.
On the second-order optimization for spiking neural networks
arXiv:2609.29379v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) offer an energy-efficient alternative to conventional neural networks by exploiting sparse, binary spikes, and event-driven computation. However, the training of SNNs remains challenging, as spiking activations create a sharp loss landscape that hinders training, and diagonal-curvature optimizers such as the Adam family may fail to capture this geometry. The extension of curvature-based optimization methods to SNNs is further complicated by the sparse, discrete, and temporally recurrent nature of their underlying dynamics. To address these limitations, we propose SpiKFAX, a second-order optimization method that formulates a computationally tractable, Kronecker-factored approximation of the Fisher information matrix specifically adapted to the structure of SNNs. Empirical evaluation across five architectures and seven datasets demonstrates that SpiKFAX consistently yields improvements in test accuracy and training stability relative to other popular optimizers.
Abstract:Spiking Neural Networks (SNNs) offer an energy-efficient alternative to conventional neural networks by exploiting sparse, binary spikes, and event-driven computation. However, the training of SNNs remains challenging, as spiking activations create a sharp loss landscape that hinders training, and diagonal-curvature optimizers such as the Adam family may fail to capture this geometry. The extension of curvature-based optimization methods to SNNs is further complicated by the sparse, discrete, and temporally recurrent nature of their underlying dynamics. To address these limitations, we propose SpiKFAX, a second-order optimization method that formulates a computationally tractable, Kronecker-factored approximation of the Fisher information matrix specifically adapted to the structure of SNNs. Empirical evaluation across five architectures and seven datasets demonstrates that SpiKFAX consistently yields improvements in test accuracy and training stability relative to other popular optimizers.
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2609.29379 [cs.LG]
(or arXiv:2609.29379v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.29379 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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