{
  "annotation_id": "e79e0077-9bc0-4849-b9d3-b730851f1cec",
  "date_created": "2026-02-17T05:53:11.744000Z",
  "date_modified": "2026-02-17T05:53:11.744000Z",
  "file_hash": "569cba7b188fcf260b07c3fc2afe5b427b96eebeed230090c53157e3b74eb1f4",
  "private": false,
  "record": {
    "abstract": "The hardware-friendly implementation of transcendental functions remains a longstanding challenge in design automation. These functions, which cannot be expressed as finite combinations of algebraic operations, pose significant complexity in digital circuit design. This study introduces a novel approach, TranSC, that utilizes stochastic computing (SC) for lightweight yet accurate implementation of transcendental functions. Building on established SC techniques, our method explores alternative random sources-specifically, quasi-random Van der Corput low-discrepancy (LD) sequences-instead of conventional pseudo-randomness. This shift enhances both the accuracy and efficiency of SC-based computations. We validate our approach through extensive experiments on various function types, including trigonometric, hyperbolic, and activation functions. The proposed design approach significantly reduces MSE by up to 98% compared to the state-of-the-art solutions while reducing hardware area, power consumption, and energy usage by 33%, 72%, and 64%, respectively.",
    "arxiv_id": "2601.07172",
    "authors": [
      "Mehran Moghadam",
      "Sercan Aygun",
      "M. Hassan Najafi"
    ],
    "categories": [
      "cs.ET",
      "cs.RO",
      "cs.SY",
      "eess.SY"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "TranSC: Hardware-Aware Design of Transcendental Functions Using Stochastic Logic",
    "url": "https://arxiv.org/abs/2601.07172",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "061465d8-53a7-435a-bc96-81e0f3a921d6",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}