Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation
Authors: Cong Guo, Yuxian Qiu, Jingwen Leng, Xiaotian Gao, Chen Zhang, Yunxin Liu, Fan Yang, Yuhao Zhu, Minyi Guo
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For demonstrating the strength of SQuant, we evaluate the SQuant as well as four SOTA methods, DFQ (Nagel et al., 2019), Zero Q (Cai et al., 2020), DSG (Zhang et al., 2021; Qin et al., 2021), and GDFQ (Xu et al., 2020), with 5 different CNN models including Res Net-18 & 50 (He et al., 2016), Inception V3 (Szegedy et al., 2016), Squeeze Next (Gholami et al., 2018) and Shuffle Net (Zhang et al., 2018) on the golden standard dataset Image Net (Krizhevsky et al., 2012). |
| Researcher Affiliation | Collaboration | Cong Guo1,2, Yuxian Qiu1,2, Jingwen Leng1,2, , Xiaotian Gao3, Chen Zhang4, Yunxin Liu5, Fan Yang3, Yuhao Zhu6 & Minyi Guo1,2, 1 Shanghai Jiao Tong University, 2 Shanghai Qi Zhi Institute 3 Microsoft Research, 4 DAMO Academy, Alibaba Group 5 Institute for AI Industry Research (AIR), Tsinghua University, 6 University of Rochester |
| Pseudocode | Yes | Algorithm 1: Progressive SQuant Algorithm. Input: Weight tensor W of layer ℓ, scale factor s of layer ℓ. Output: Quantized weight tensor C of layer ℓ. ... Algorithm 2: SQuant Flip Algorithm. Input: Rounded/SQuanted Weight w; Weight perturbation p. Output: Updated Quantized Weight w. |
| Open Source Code | Yes | We have open-sourced the SQuant framework1. 1https://github.com/clevercool/SQuant |
| Open Datasets | Yes | on the golden standard dataset Image Net (Krizhevsky et al., 2012). |
| Dataset Splits | No | The paper evaluates on the Image Net dataset but does not explicitly state the training/test/validation dataset splits (e.g., specific percentages or sample counts) used for their experiments, nor does it reference predefined splits with citations for their specific setup. |
| Hardware Specification | Yes | All DFQ algorithms are implemented with Py Torch (Paszke et al., 2019) and evaluated on Nvidia GPU A100-40GB. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2019)' but does not specify a version number for PyTorch or any other software dependencies required to reproduce the experiments. |
| Experiment Setup | Yes | Unless otherwise stated, we employ both weight and activation quantization in all experiments. Also, uniform quantization grids are used in all experiments, and hyper-parameters, e.g., re = rk = 1.0 and rc = 0.5, for all SQuant experiments are the same. |