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..
E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings
Authors: Yue Wang, Ziyu Jiang, Xiaohan Chen, Pengfei Xu, Yang Zhao, Yingyan Lin, Zhangyang Wang
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive simulations and ablation studies, with real energy measurements from an FPGA board, confirm the superiority of our proposed strategies and demonstrate remarkable energy savings for training. |
| Researcher Affiliation | Academia | Yue Wang , , Ziyu Jiang , , Xiaohan Chen , , Pengfei Xu , Yang Zhao , Yingyan Lin and Zhangyang Wang Department of Computer Science and Engineering, Texas A&M University Department of Electrical and Computer Engineering, Rice University EMAIL EMAIL |
| Pseudocode | No | The proposed framework |
| Open Source Code | No | https://rtml.eiclab.net/?page_id=120 |
| Open Datasets | Yes | Datasets: We evaluate our proposed techniques on two datasets: CIFAR-10 and CIFAR-100. |
| Dataset Splits | No | Datasets: We evaluate our proposed techniques on two datasets: CIFAR-10 and CIFAR-100. Common data augmentation methods (e.g., mirroring/shifting) are adopted, and data are normalized as in [60]. |
| Hardware Specification | Yes | Specifically, unless otherwise specified, all the energy or energy savings are obtained through real measurements by training the corresponding models and datasets in a state-of-the-art FPGA [65], which is a digilent Zed Board Zynq-7000 ARM/FPGA So C Development Board. |
| Software Dependencies | No | Specifically, we use an SGD with a momentum of 0.9 and a weight decaying factor of 0.0001, and the initialization introduced in [63]. Models are trained for 64k iterations. |
| Experiment Setup | Yes | Specifically, we use an SGD with a momentum of 0.9 and a weight decaying factor of 0.0001, and the initialization introduced in [63]. Models are trained for 64k iterations. For experiments where PSG is used, the initial learning rate is adjusted to 0.03 as Sign SGD[20] suggested small learning rates to benefit convergence. For others, the learning rate is initially set to be 0.1 and then decayed by 10 at the 32k and 48k iterations, respectively. |