TinyML models often operate in remote, dynamic environments without cloud connectivity, making them prone to failures. Ensuring reliability in such scenarios requires not only detecting model failures but also identifying their root causes. However, transient failures, privacy concerns, and the safety-critical nature of many applications-where systems cannot be interrupted for debugging-complicate the use of raw sensor data for offline analysis. We propose DEBUG-HD, a novel, resource-efficient on-device debugging approach optimized for KB-sized tinyML devices that utilizes hyper-dimensional computing (HDC). Our method introduces a new HDC encoding technique that leverages conventional neural networks, allowing DEBUG-HD to outperform prior binary HDC methods by 27% on average in detecting input corruptions across various image and audio datasets.

翻译：微型机器学习（TinyML）模型常在无云端连接的远程动态环境中运行，极易发生故障。在此类场景中确保可靠性不仅需要检测模型故障，还需定位其根本原因。然而，瞬时性故障、隐私顾虑以及许多应用的安全关键特性——系统无法因调试而中断——使得利用原始传感器数据进行离线分析变得复杂。本文提出DEBUG-HD，一种专为KB级微型ML设备优化的新型资源高效设备端调试方法，该方法采用超维计算（HDC）。我们提出一种创新的HDC编码技术，该技术利用传统神经网络，使DEBUG-HD在多种图像与音频数据集的输入损坏检测任务中，平均性能较现有二进制HDC方法提升27%。