Rapid pipeline for training and deploying ML models on the WeBe Band (arXiv:2609.29084v1)
The paper introduces an automated workflow that integrates the open-source Piccolo AI ecosystem to generate hardware-efficient machine-learning models for the wrist-worn WeBe Band, supporting AutoML, hardware-aware quantization, on-device profiling, and over-the-air firmware deployment. The system targets microcontroller constraints, supports classical ML and lightweight neural networks, and emphasizes rapid iteration and real-world latency/memory evaluation rather than new architectures.