mlsanityData to 14 Oct 2026

DEBUG-HD: Debugging TinyML models on-device using Hyper-Dimensional computing

arXiv 2411.10692 ↗First posted 16 Nov 2024cs.LG cs.AI cs.NEcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 61 references that are in the map. First posted on arXiv 16 Nov 2024.

Builds on

23 of 61 references.

References · 23

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  3. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  4. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
  5. 2013Visualizing and Understanding Convolutional Networkscited by 2,290
  6. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,319
  7. 2014Striving for Simplicity: The All Convolutional Netcited by 990
  8. 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 752
  9. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  10. 2019Selection via Proxy: Efficient Data Selection for Deep Learningcited by 339
  11. 2022On-Device Training Under 256KB Memorycited by 160
  12. 2017Hello Edge: Keyword Spotting on Microcontrollerscited by 110
11 more
  1. 2017Learning how to explain neural networks: PatternNet and PatternAttributioncited by 84
  2. 2021TinyOL: TinyML with Online-Learning on Microcontrollerscited by 65
  3. 2021Meaningfully Debugging Model Mistakes using Conceptual Counterfactual Explanationscited by 53
  4. 2020Model Assertions for Monitoring and Improving ML Modelscited by 22
  5. 2022Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architecturescited by 17
  6. 2023DistHD: A Learner-Aware Dynamic Encoding Method for Hyperdimensional Classificationcited by 13
  7. 2020Characterizing and Taming Model Instability Across Edge Devicescited by 7
  8. 2021ML-EXray: Visibility into ML Deployment on the Edgecited by 6
  9. 2023Monitoring and Adapting ML Models on Mobile Devicescited by 6
  10. 2023U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoTcited by 5
  11. 2020A System for Real-Time Interactive Analysis of Deep Learning Trainingcited by 1

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Ariel-ML: Computing Parallelization with Embedded Rust for Neural Networks on Heterogeneous Multi-core Microcontrollerscited by 1