{
  "annotation_id": "53b4f036-ce1c-4fea-988e-844fda7b5b11",
  "date_created": "2026-02-17T05:53:16.236000Z",
  "date_modified": "2026-02-17T05:53:16.236000Z",
  "file_hash": "6d046588d42990289356ff7f639c6ad1e2d2e7017fb3352c0a728810d09ee94b",
  "private": false,
  "record": {
    "abstract": "This work presents Bio-RV, a compact and resource-efficient RISC-V processor intended for biomedical control applications, such as accelerator-based biomedical SoCs and implantable pacemaker systems. The proposed Bio-RV is a multi-cycle RV32I core that provides explicit execution control and external instruction loading with capabilities that enable controlled firmware deployment, ASIC bring-up, and post-silicon testing. In addition to coordinating accelerator configuration and data transmission in heterogeneous systems, Bio-RV is designed to function as a lightweight host controller, handling interfaces with pacing, sensing, electrogram (EGM), telemetry, and battery management modules. With 708 LUTs and 235 flip-flops on FPGA prototypes, Bio-RV, implemented in a 180 nm CMOS technology, operate at 50 MHz and feature a compact hardware footprint. According to post-layout results, the proposed architectural decisions align with minimal energy use. Ultimately, Bio-RV prioritises deterministic execution, minimal hardware complexity, and integration flexibility over peak computing speed to meet the demands of ultra-low-power, safety-critical biomedical systems.",
    "arxiv_id": "2601.08428",
    "authors": [
      "Vijay Pratap Sharma",
      "Annu Kumar",
      "Mohd Faisal Khan",
      "Mukul Lokhande",
      "Santosh Kumar Vishvakarma"
    ],
    "categories": [
      "eess.SP",
      "cs.AR"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Bio-RV: Low-Power Resource-Efficient RISC-V Processor for Biomedical Applications",
    "url": "https://arxiv.org/abs/2601.08428",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "ca318b4d-fa06-43c6-a545-622531b1c7ef",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}