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..
Computing Circuits Optimization via Model-Based Circuit Genetic Evolution
Authors: Zhihai Wang, Jie Wang, Xilin Xia, Dongsheng Zuo, Lei Chen, Yuzhe Ma, Jianye HAO, Mingxuan Yuan, Feng Wu
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate MUTE on several fundamental computing circuits, including multipliers, adders, and multiply-accumulate circuits. Experiments on these circuits demonstrate that MUTE significantly Pareto-dominates state-of-the-art approaches in terms of both area and delay. (Abstract) and Section 5 EXPERIMENTS |
| Researcher Affiliation | Collaboration | 1Mo E Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China 2 Noah s Ark Lab, Huawei Technologies 3 Microelectronics Thrust, Hong Kong University of Science and Technology (Guangzhou) 4 College of Intelligence and Computing, Tianjin University |
| Pseudocode | No | The paper describes methods in prose and figures (e.g., Figure 3 illustrates the MUTE framework) but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | 2. Source Code. To facilitate the evaluation process and support a thorough review, we have released our source code at the following link: https://anonymous.4open.science/r/AI4MUL-4199. |
| Open Datasets | Yes | Throughout our experiments, we utilize the Open ROAD flow (Ajayi & Blaauw, 2019) alongside the Nan Gate 45nm open-cell library (Nangate Inc., 2008) for circuit synthesis, coupled with Open STA (Parallax Software Inc.) for timing analysis. (Section 5.1) and Nangate45 is a widely used standard cell library in the semiconductor industry. It is open source and free, and we can obtain it at https://silvaco.com/services/library-design/ (Appendix F.2) |
| Dataset Splits | No | The paper evaluates MUTE on different problem instances (e.g., '8-bit, 16-bit, 32-bit, and 64-bit multipliers') rather than using a single dataset with explicit training, validation, and test splits. No specific dataset split information is provided. |
| Hardware Specification | Yes | Our experiments were executed on a Linux-based system equipped with a 3.60 GHz Intel Xeon Gold 6246R CPU and NVIDIA RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions software such as 'Open ROAD flow', 'Nan Gate 45nm open-cell library', 'Open STA', 'Adam optimizer', and 'Py Torch framework' but does not provide specific version numbers for these software dependencies, only citation years for some. |
| Experiment Setup | Yes | Table 5: Common parameters used in the comparative evaluation and ablation study. Learning-Based Population Initialization Module: environment steps per learning episode 25, policy updates per environment step 1, optimizer Adam, discount (𝛾) 0.8, total learning episodes for initialization 40. Genetic Variation Module: samples generated by sequential mutation operator at each iteration 100, samples generated by genetic crossover operator at each iteration 200, total iterations for evolution 400. Model-Based Module: samples for circuit synthesis evaluation at each iteration 5. |