在WeBe Band上训练和部署机器学习模型的快速流水线
A Rapid Pipeline for Training and Deploying ML Models on WeBe Band
- UC Davis（加州大学戴维斯分校）
- Quandary Peak Research
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种快速流水线，用于在WeBe Band可穿戴设备上训练和部署ML模型，通过AutoML、量化及OTA部署实现硬件高效模型，并验证了模型复杂度与可部署性的权衡。
AI中文摘要：
在计算和内存资源有限的边缘设备上开发优化的机器学习算法具有挑战性、耗时且高度依赖于设备特定约束。在这项工作中，我们简化了边缘机器学习工作流程，以实现在WeBe Band上直接快速开发、优化和部署机器学习（ML）模型。WeBe Band是一款专为多模态生理数据监测设计的腕戴式可穿戴设备。所提出的系统自动生成硬件高效的ML模型，这些模型可以轻松集成到WeBe核心固件中，支持AutoML、硬件感知量化和性能分析，以构建满足预期延迟目标同时兼容设备内存和功耗限制的模型。所提出的框架将开源Piccolo AI生态系统与自动化流水线紧密集成，该流水线生成可部署的固件工件，执行硬件感知模型编译，并支持空中（OTA）部署。该系统支持多种轻量级模型类别，包括经典机器学习算法和神经网络，并提供内置的片上分析工具，以在真实执行条件下评估推理延迟和内存占用。实验结果表明，在微控制器上模型复杂性和可部署性之间存在明显的权衡，表明经典模型提供强大的实时性能，而轻量级神经网络需要仔细的资源管理。当前工作主要侧重于系统级自动化、可部署性以及使研究人员和开发者能够快速迭代模型并直接在目标硬件上评估它们，而不是提出新的学习架构。尽管在WeBe Band平台上进行了演示，但该工作流程被设计为可扩展到其他支持ML的边缘设备。
英文摘要：
Developing optimized machine-learning algorithms for edge devices with limited computational and memory resources is challenging, time-consuming, and highly dependent on device-specific constraints. In this work, we streamline an edge ML workflow to enable rapid development, optimization, and deployment of machine-learning (ML) models directly on the WeBe Band, a wrist-worn wearable device designed for multimodal physiological data monitoring. The proposed system automatically generates hardware-efficient ML models that can be easily integrated into the WeBe core firmware, supporting AutoML, hardware-aware quantization, and performance profiling to build models that meet desired latency targets while remaining compatible with device memory and power limitations. The proposed framework tightly integrates the open-source Piccolo AI ecosystem with an automated pipeline that generates deployable firmware artifacts, performs hardware-aware model compilation, and supports over-the-air (OTA) deployment. The system supports multiple lightweight model classes, including classical machine-learning algorithms and neural networks, and provides built-in on-device profiling tools to evaluate inference latency and memory footprint under realistic execution conditions. Experimental results demonstrate clear trade-offs between model complexity and deployability on a microcontroller, showing that classical models offer strong real-time performance while lightweight neural networks require careful resource management. Rather than proposing new learning architectures, the current work mainly focuses on system-level automation, deployability, and enabling researchers and developers to rapidly iterate on models and evaluate them directly on target hardware. Although demonstrated on the WeBe Band platform, the workflow is designed to be extensible to other ML-powered edge devices.
