{
  "id": "2408.01283",
  "version": "v1",
  "published": "2024-08-02T14:09:39.000Z",
  "updated": "2024-08-02T14:09:39.000Z",
  "title": "A Tiny Supervised ODL Core with Auto Data Pruning for Human Activity Recognition",
  "authors": [
    "Hiroki Matsutani",
    "Radu Marculescu"
  ],
  "comment": "IEEE BSN 2024 (accepted)",
  "categories": [
    "cs.LG",
    "cs.AR"
  ],
  "abstract": "In this paper, we introduce a low-cost and low-power tiny supervised on-device learning (ODL) core that can address the distributional shift of input data for human activity recognition. Although ODL for resource-limited edge devices has been studied recently, how exactly to provide the training labels to these devices at runtime remains an open-issue. To address this problem, we propose to combine an automatic data pruning with supervised ODL to reduce the number queries needed to acquire predicted labels from a nearby teacher device and thus save power consumption during model retraining. The data pruning threshold is automatically tuned, eliminating a manual threshold tuning. As a tinyML solution at a few mW for the human activity recognition, we design a supervised ODL core that supports our automatic data pruning using a 45nm CMOS process technology. We show that the required memory size for the core is smaller than the same-shaped multilayer perceptron (MLP) and the power consumption is only 3.39mW. Experiments using a human activity recognition dataset show that the proposed automatic data pruning reduces the communication volume by 55.7% and power consumption accordingly with only 0.9% accuracy loss.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-08-02T14:09:39.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "human activity recognition",
      "tiny supervised odl core",
      "auto data pruning",
      "automatic data pruning",
      "power consumption"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}