Towards transferable lightweight neuromorphic computing through a model-free temporal-switch framework
Lightweight neuromorphic computing offers a promising route to efficient AI, with particular benefits for resource-constrained edge deployments. However, its scalable deployment that can reliably transfer the expected performance has long been hindered by device-to-device variations, which necessitate costly and repeated re-training on new copies and undermine the practical advantages. To address this issue, we introduce a model-free temporal-switch (TS) framework to improve the direct transfer performance, without post-training calibration or adjustment. The TS framework provides a methodology to incorporate a broader spectrum of devices in the training process. In the validation using m...
| Authors | Zefeng Zhang, Chao Li, Siyao Chen et al. |
|---|---|
| Primary category | cs.AR |
| Published | 1 July 2026 |
| Last updated at source | 18 July 2026 |
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