{
  "id": 15538085,
  "title": "qGAMMA: A Framework for Optimal DNN Mapping on Multi-Precision Accelerators",
  "url": "https://urgent.news/2026/10/16/qgamma-a-framework-for-optimal-dnn-mapping-on-multi-precision",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-16T09:54:13.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.23222v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "This work describes qGAMMA, a flexible framework for exploring and optimizing the mapping of mixed-precision Deep Neural Network (DNN) models onto general-purpose spatial accelerators that support a range of fixed- and floating-point precisions. qGAMMA navigates the design space using a domain-specific genetic algorithm and an analytical performance model based on hardware synthesis results. The…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}