pith. sign in

arxiv: 1712.05877 · v1 · pith:RYMD2RJ5new · submitted 2017-12-15 · 💻 cs.LG · stat.ML

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

Benoit Jacob , Skirmantas Kligys , Bo Chen , Menglong Zhu , Matthew Tang , Andrew Howard , Hartwig Adam , Dmitry Kalenichenko This is my paper
classification 💻 cs.LG stat.ML
keywords inferencequantizationaccuracyefficientinteger-onlymodelon-devicescheme
0
0 comments X
read the original abstract

The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a quantization scheme that allows inference to be carried out using integer-only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware. We also co-design a training procedure to preserve end-to-end model accuracy post quantization. As a result, the proposed quantization scheme improves the tradeoff between accuracy and on-device latency. The improvements are significant even on MobileNets, a model family known for run-time efficiency, and are demonstrated in ImageNet classification and COCO detection on popular CPUs.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Llamas on the Web: Memory-Efficient, Performance-Portable, and Multi-Precision LLM Inference with WebGPU

    cs.DC 2026-05 conditional novelty 7.0

    LlamaWeb is a WebGPU backend for llama.cpp that uses static memory planning, tunable kernels, and templated multi-precision support to cut memory use by 29-33% and raise decode throughput by 45-69% versus prior browse...

  2. LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

    cs.LG 2022-08 conditional novelty 7.0

    LLM.int8() performs 8-bit inference for transformers up to 175B parameters with no accuracy loss by combining vector-wise quantization for most features with 16-bit mixed-precision handling of systematic outlier dimensions.

  3. LoKA: Low-precision Kernel Applications for Recommendation Models At Scale

    cs.LG 2026-05 unverdicted novelty 6.0

    LoKA enables practical FP8 use in numerically sensitive large recommendation models via profiling, model adaptations, and runtime kernel orchestration.

  4. LoKA: Low-precision Kernel Applications for Recommendation Models At Scale

    cs.LG 2026-05 unverdicted novelty 6.0

    LoKA enables practical FP8 use in numerically sensitive large recommendation models via online profiling of activations, reusable model modifications for stability, and dynamic kernel dispatching.

  5. On the Quantization Robustness of Diffusion Language Models in Coding Benchmarks

    cs.LG 2026-04 unverdicted novelty 4.0

    Diffusion coding model CoDA shows smaller accuracy drops than Qwen3-1.7B under 2-4 bit quantization on HumanEval and MBPP.

  6. Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions

    cs.SE 2024-02 unverdicted novelty 4.0

    Industry AI practitioners view model quality through nine attributes with context-dependent priorities, where data imbalance is a key challenge addressed by strategies like active learning, as confirmed by interviews ...