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        "description": "Integer-only refers to deployed inference, not all training or offline scale preparation. Inherits earlier low-precision/fixed-point inference; rounding, zero-point correction, fused-operator placement and hardware kernels matter. Evaluated mobile CNN latency/accuracy tradeoffs do not guarantee lossless quantization or speedups on arbitrary models/devices.",
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      "statement": "# Integer-only inference with affine quantization and simulated-quantization training\n\nCo-designed affine scale/zero-point quantization, 8-bit weight/activation and 32-bit accumulation/bias arithmetic with floating-point fake-quantization training for efficient mobile inference.\n\n## Scientific scope\n\nInteger-only refers to deployed inference, not all training or offline scale preparation. Inherits earlier low-precision/fixed-point inference; rounding, zero-point correction, fused-operator placement and hardware kernels matter. Evaluated mobile CNN latency/accuracy tradeoffs do not guarantee lossless quantization or speedups on arbitrary models/devices.\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\nBenoit Jacob; Skirmantas Kligys; Bo Chen; Menglong Zhu; Matthew Tang; Andrew Howard; Hartwig Adam; Dmitry Kalenichenko\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- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://arxiv.org/abs/1712.05877v1)\n",
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