{
  "id": 11599,
  "url": "https://arxiv.org/abs/2607.15123v1",
  "title": "NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference",
  "summary": "Recent FPGAs have improved deep learning (DL) inference efficiency through dedicated tensor blocks and in-BRAM computation. ReRAM-based analog in-memory computing (IMC) pushes efficiency further, offering an order-of-magnitude improvement in compute density and energy efficiency over conventional digital logic by performing vector-matrix multiplication (VMM) directly within the ReRAM crossbar; prior work has integrated such IMC blocks into FPGAs for DL inference. However, conventional IMC design",
  "authors": "Jiajun Hu, Ruthwik Reddy Sunketa, Lei Zhao, Archit Gajjar, Luca Buonanno, Aman Arora",
  "category": "research",
  "topics": "environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-16T15:32:44.000Z",
  "fetched_at": "2026-07-18T05:10:55.931Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/11599",
  "original_url": "https://arxiv.org/abs/2607.15123v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}