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..
Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks
Authors: Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our evaluation shows that layer-wise parallelism outperforms state-of-the-art approaches by increasing training throughput, reducing communication costs, achieving better scalability to multiple GPUs, while maintaining original network accuracy. |
| Researcher Affiliation | Collaboration | 1Stanford University 2Microsoft. Correspondence to: Zhihao Jia <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 shows pseudocode using node and edge eliminations as subroutines to ο¬nd an optimal parallelization strategy under our cost model. |
| Open Source Code | No | The paper states 'we implemented our framework in Legion...' but does not explicitly provide a concrete access link or statement about releasing the source code for their implementation. |
| Open Datasets | Yes | We evaluate the runtime performance of all three CNNs on the Image Net-1K dataset (Deng et al., 2009) that consists of 1.2 million images from 1,000 categories. |
| Dataset Splits | No | No specific details regarding training, validation, or test dataset splits (e.g., percentages, sample counts, or explicit mention of standard splits for the used datasets) were found. |
| Hardware Specification | Yes | All experiments were performed on a GPU cluster with 4 compute nodes, each of which is equipped with two Intel 10-core E5-2600 CPUs, 256G main memory, and four NVIDIA Tesla P100 GPUs. |
| Software Dependencies | Yes | We ran data parallelism experiments in Tensor Flow r1.7, Py Torch v0.3, and our implementation and compared the runtime performance. |
| Experiment Setup | Yes | We use synchronous training and a per-GPU batch size of 32 for all experiments. |