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..
NeuroSchedule: A Novel Effective GNN-based Scheduling Method for High-level Synthesis
Authors: Jun Zeng, Mingyang Kou, Hailong Yao
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that Neuro Schedule obtains near-optimal solutions while achieving more than 50,000 improvement in runtime compared with the ILP-based scheduling method. At the same time, Neuro Schedule improves the scheduling results by 6.10% on average compared with state-of-the-art entropy-directed method. |
| Researcher Affiliation | Collaboration | Jun Zeng Tsinghua University Mingyang Kou Tsinghua University Hailong Yao Tsinghua University Xu-Cheng Yin University of Science and Technology Beijing Haili Wang Hercules Microelectronics Co., Ltd |
| Pseudocode | Yes | Algorithm 1 Neuro Schedule algorithm. |
| Open Source Code | No | Our Neuro Schedule will be open-sourced once the paper is accepted. |
| Open Datasets | Yes | To train the GNN model, we build a dataset including 50,000 CDFGs. ... The benchmarks used in our paper are open-sourced, and we cite them in our paper. |
| Dataset Splits | No | The paper describes generating datasets and evaluating models on them, but does not provide explicit training/validation/test dataset splits (e.g., percentages or absolute counts) for reproducibility. |
| Hardware Specification | Yes | We conduct all the experiments on a Ubuntu 20.04 LTS Linux Server with a CPU (Intel(R) Xeon(R) Gold 5218 CPU@2.30GHz) and a GPU (NVIDIA Tesla V100). |
| Software Dependencies | Yes | The proposed GNN-based scheduler is implemented in Python (version 3.9.12), and the GNN model is designed and trained with Pytorch [22] (version 1.8.0) and py G [23] (version 2.0.4). For efficiency, Gurobi [24] is adopted as the ILP solver. |
| Experiment Setup | Yes | As depicted in Figure 4, the proposed model outputs the operations priorities by regression. To effectively train the proposed model, a dedicated training pipeline is proposed. The proposed training pipeline mainly focuses on two questions: 1. How to acquire the regression labels; 2. How to select the training objective function (a.k.a loss function). |