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MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. (arXiv:2111.03759v2 [cs.LG] UPDATED)
Jan. 26, 2022, 2:11 a.m. | Yuhang Li, Mingzhu Shen, Jian Ma, Yan Ren, Mingxin Zhao, Qi Zhang, Ruihao Gong, Fengwei Yu, Junjie Yan
cs.LG updates on arXiv.org arxiv.org
Model quantization has emerged as an indispensable technique to accelerate
deep learning inference. While researchers continue to push the frontier of
quantization algorithms, existing quantization work is often unreproducible and
undeployable. This is because researchers do not choose consistent training
pipelines and ignore the requirements for hardware deployments. In this work,
we propose Model Quantization Benchmark (MQBench), a first attempt to evaluate,
analyze, and benchmark the reproducibility and deployability for model
quantization algorithms. We choose multiple different platforms for real-world …
More from arxiv.org / cs.LG updates on arXiv.org
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