The paper presents TASTE, a method that uses Bayesian optimization to tune batch size for on‑device edge learning, aiming to maximize hardware throughput while preserving accuracy. Experiments on devices like the Raspberry Pi 4 show that the tuned batch size, combined with gradient accumulation and linear learning‑rate scaling, can double training throughput compared to using the maximum batch size. In online continual learning, the optimal batch size also helps balance stability and plasticity, reducing catastrophic forgetting without sacrificing efficiency.
By Avik Bhatnagar, Federico Nicolas Peccia, Oliver Bringmann
arXiv:2509. 13211v4 Announce Type: replace Abstract: The ability to learn continuously over time remains a major challenge for modern machine learning systems, even in the era of Foundation Models.
By Irene Testa, Luigi Quarantiello, Eric Nuertey Coleman, Samrat Mukherjee, Julio Hurtado, Vincenzo Lomonaco
The paper introduces sLoTh, a parameter‑efficient continual learning framework for sparse event‑based vision transformers. By freezing the backbone and limiting plasticity to low‑rank attention updates (seLoRA) and shared neuronal threshold modulation, sLoTh adapts to new tasks while updating less than 1% of the parameters and avoiding replay buffers. Experiments on CIFAR‑100, Tiny‑ImageNet, ImageNet‑100, and ImageNet‑R show competitive rehearsal‑free performance across up to 100 tasks and achieve roughly 6.5× lower energy consumption than dense vision transformers.
arXiv:2603. 10046v2 Announce Type: replace Abstract: Wearable sensors in Internet of Things (IoT) ecosystems increasingly support applications such as remote health monitoring, elderly care, and smart home automation, all of which rely on robust human activity recognition (HAR).
By Reza Rahimi Azghan, Gautham Krishna Gudur, Mohit Malu, Edison Thomaz, Giulia Pedrielli, Pavan Turaga, Hassan Ghasemzadeh
The paper introduces sLoTh, a parameter‑efficient continual learning framework for sparse event‑based vision transformers. sLoTh freezes the backbone and limits plasticity to low‑rank attention updates (seLoRA) and shared neuronal threshold modulation, updating less than 1% of parameters without replay buffers. Experiments on CIFAR‑100, Tiny‑ImageNet, ImageNet‑100, and ImageNet‑R show competitive rehearsal‑free performance across up to 100 tasks while achieving roughly 6.5× lower energy consumption than dense vision transformers.
By Vaishnavi Nagabhushana, Kartikay Agrawal, Ayon Borthakur
arXiv:2604. 07396v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) inference on edge Neural Processing Units (NPUs) is fundamentally constrained by limited on-chip memory capacity.
By Jintao Zhang, Xuanyao Fong