Practical Mechanism for Fault-Tolerant Spiking Neural Networks via Simple Input Control Based on Learnable Fragmentation

Hyun-Jong Lee, Jae-Han Lim
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:64616-64653, 2026.

Abstract

Spiking Neural Networks (SNNs) are regarded as the third generation of neural networks, offering energy-efficient computing for neuromorphic devices. Despite this benefit, hardware-implemented SNNs are vulnerable to hardware faults, which severely degrade their performance. Previous approaches have required direct access to internal SNN circuits to modify weights or monitor internal states, limiting their practicality. Improving robustness to hardware faults without such access remains challenging. To overcome this challenge, we propose a fault-tolerant mechanism that operates only through input data control. Hardware faults reduce the usable learning capacity of SNNs, resulting in a mismatch between the instantaneous input load and the degraded network dynamics. Our mechanism mitigates this mismatch by dividing each input sample into multiple fragments, redistributing the input load via a learnable fragmentation strategy. The strategy learns two key fragmentation components: 1) division boundaries and 2) the number of fragments. To our knowledge, this is the first mechanism to improve the fault tolerance of SNNs without accessing the internal circuits. Experimental results demonstrate that our mechanism consistently outperforms previous methods in various SNN models, achieving these gains without direct access to internal circuits. Furthermore, we validate its effectiveness on SNNs implemented with a physical FPGA platform, confirming its practicality.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-lee26ar, title = {Practical Mechanism for Fault-Tolerant Spiking Neural Networks via Simple Input Control Based on Learnable Fragmentation}, author = {Lee, Hyun-Jong and Lim, Jae-Han}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {64616--64653}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/lee26ar/lee26ar.pdf}, url = {https://proceedings.mlr.press/v306/lee26ar.html}, abstract = {Spiking Neural Networks (SNNs) are regarded as the third generation of neural networks, offering energy-efficient computing for neuromorphic devices. Despite this benefit, hardware-implemented SNNs are vulnerable to hardware faults, which severely degrade their performance. Previous approaches have required direct access to internal SNN circuits to modify weights or monitor internal states, limiting their practicality. Improving robustness to hardware faults without such access remains challenging. To overcome this challenge, we propose a fault-tolerant mechanism that operates only through input data control. Hardware faults reduce the usable learning capacity of SNNs, resulting in a mismatch between the instantaneous input load and the degraded network dynamics. Our mechanism mitigates this mismatch by dividing each input sample into multiple fragments, redistributing the input load via a learnable fragmentation strategy. The strategy learns two key fragmentation components: 1) division boundaries and 2) the number of fragments. To our knowledge, this is the first mechanism to improve the fault tolerance of SNNs without accessing the internal circuits. Experimental results demonstrate that our mechanism consistently outperforms previous methods in various SNN models, achieving these gains without direct access to internal circuits. Furthermore, we validate its effectiveness on SNNs implemented with a physical FPGA platform, confirming its practicality.} }
Endnote
%0 Conference Paper %T Practical Mechanism for Fault-Tolerant Spiking Neural Networks via Simple Input Control Based on Learnable Fragmentation %A Hyun-Jong Lee %A Jae-Han Lim %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-lee26ar %I PMLR %P 64616--64653 %U https://proceedings.mlr.press/v306/lee26ar.html %V 306 %X Spiking Neural Networks (SNNs) are regarded as the third generation of neural networks, offering energy-efficient computing for neuromorphic devices. Despite this benefit, hardware-implemented SNNs are vulnerable to hardware faults, which severely degrade their performance. Previous approaches have required direct access to internal SNN circuits to modify weights or monitor internal states, limiting their practicality. Improving robustness to hardware faults without such access remains challenging. To overcome this challenge, we propose a fault-tolerant mechanism that operates only through input data control. Hardware faults reduce the usable learning capacity of SNNs, resulting in a mismatch between the instantaneous input load and the degraded network dynamics. Our mechanism mitigates this mismatch by dividing each input sample into multiple fragments, redistributing the input load via a learnable fragmentation strategy. The strategy learns two key fragmentation components: 1) division boundaries and 2) the number of fragments. To our knowledge, this is the first mechanism to improve the fault tolerance of SNNs without accessing the internal circuits. Experimental results demonstrate that our mechanism consistently outperforms previous methods in various SNN models, achieving these gains without direct access to internal circuits. Furthermore, we validate its effectiveness on SNNs implemented with a physical FPGA platform, confirming its practicality.
APA
Lee, H. & Lim, J.. (2026). Practical Mechanism for Fault-Tolerant Spiking Neural Networks via Simple Input Control Based on Learnable Fragmentation. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:64616-64653 Available from https://proceedings.mlr.press/v306/lee26ar.html.

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