{
  "id": 9469583,
  "title": "MicroQonv: Reshaping Convolution Tensors for Efficient Microscaling in Training and Inference",
  "url": "https://urgent.news/2026/09/23/microqonv-reshaping-convolution-tensors-for-efficient-microscaling-in",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-23T16:30:48.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.28358v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Microscaling quantization techniques are increasingly used to represent neural network parameters with 8 bits or fewer while preserving near-full precision accuracy. However, applying these methods efficiently in convolutional layers is not straightforward. A naive approach transfers full-precision weights and activations to processing units and quantizes each tensor twice, resulting in much more…",
  "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."
}