arXiv:2609.36584v1 Announce Type: new
Abstract: Analog in-memory computing (AIMC) offers an alternative for model training by executing matrix operations directly where weights are stored. However, s...
By Zhaoxian Wu, Tayfun Gokmen, Omobayode Fagbohungbe, T. Patrick Xiao, Tianyi Chen
arXiv:2607. 28418v1 Announce Type: cross Abstract: Pruning is a promising approach for improving the efficiency of LLMs.
By Haozhe Hu, Hao Wu, Peiran Yin, Chao Han, Yunpu Ma, Xiaoyu Shen
TinyUDE introduces a solver‑free training framework for Universal Differential Equations (UDEs) that fits a hybrid vector field directly to the first and second time‑derivatives of observed system states using Lie‑Taylor jet matching. The method estimates these derivatives online via Savitzky‑Golay filtering, enabling fully analytic gradients without automatic differentiation. Experiments on damped and chaotic pendulum systems show that TinyUDE matches or surpasses conventional solver‑based baselines while using less than 108 kB of static memory and achieving real‑time on‑device training on an ESP32 microcontroller.
By Pranavanath Balamurali, Hrishi Kamireddy
arXiv:2607. 18306v1 Announce Type: cross Abstract: Sharpness-Aware Minimization (SAM) improves generalization by minimizing the worst-case loss in a local parameter neighborhood.
By Zhen Huang, Jiaxin Deng, Junbiao Pang
arXiv:2606. 15004v1 Announce Type: cross Abstract: Deploying neural networks on low-power microcontrollers (MCUs) requires selecting model architectures under tight memory, latency, and energy constraints.
By Joseph Q. Zales, Pragya Sharma, Mani Srivastava
arXiv:2606. 17249v1 Announce Type: cross Abstract: The dominant trajectory of modern machine learning has been to scale up: larger models, larger accelerators, larger memory budgets.
By Emre Can Kizilates