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..
Toward Efficient Low-Precision Training: Data Format Optimization and Hysteresis Quantization
Authors: Sunwoo Lee, Jeongwoo Park, Dongsuk Jeon
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We measure the training performance of this format and compare it against other 8-bit data formats from recent studies by applying those formats to the training of various neural network models. |
| Researcher Affiliation | Academia | Sunwoo Lee, Jeongwoo Park, Dongsuk Jeon Graduate School of Convergence Science and Technology Seoul National University, Seoul, Korea EMAIL |
| Pseudocode | No | The paper describes the methods textually and with mathematical equations, but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | We created a package named lptorch, short for low precision Py Torch, and the code can be found in the supplementary material. |
| Open Datasets | Yes | Training loss is obtained by training Res Net-18 on CIFAR-10 dataset using SGD with a momentum of 0.9 for 60 epochs. |
| Dataset Splits | Yes | Fig. 1(b) shows the Top-1 validation accuracy of Res Net-18 (He et al., 2016) trained on Image Net |
| Hardware Specification | No | The paper discusses hardware implementation costs for MAC units (e.g., 'Synthesized in 40nm Process', 'FPGA (XC7A100TCSG324-1)') related to the proposed formats, but it does not specify the hardware (e.g., GPU, CPU models) used to run the neural network training experiments. |
| Software Dependencies | No | The paper mentions software like 'Py Torch', 'C++ and CUDA codes', 'Python APIs', 'Fair Seq', and 'SGD' but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | We conducted Image Net experiments using SGD with a momentum of 0.9 for 90 epochs with a batch size of 256 images and an initial learning rate of 0.1 which is decayed by a factor of 10 at the 30th and 60th epochs. |