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      "statement": "# FBGEMM: fused quantized inference kernels with runtime specialization\n\nFused quantization-related packing, row-offset computation and output processing with CPU matrix multiplication, using shape-specific runtime code generation and cached kernels to accelerate inference.\n\n## Scientific scope\n\n2021 is the cited technical-report release, not a claim that deployment or kernel work began then. This report concerns CPU FBGEMM, not all later FBGEMM_GPU work. It leaves threading orchestration to callers; INT16 accumulation can overflow and needs the stated handling. Speed and accuracy depend on shape, hardware and quantization.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nDaya Khudia; Jianyu Huang; Protonu Basu; Summer Deng; Haixin Liu; Jongsoo Park; Mikhail Smelyanskiy\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference](https://arxiv.org/abs/2101.05615v1)\n",
      "summary": "Fused quantization-related packing, row-offset computation and output processing with CPU matrix multiplication, using shape-specific runtime code generation and cached kernels to accelerate inference.",
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