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..
Towards Automated RISC-V Microarchitecture Design with Reinforcement Learning
Authors: Chen Bai, Jianwang Zhai, Yuzhe Ma, Bei Yu, Martin D. F. Wong
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | experiments using commercial electronic design automation (EDA) tools show that our method achieves an average PPA trade-off improvement of 16.03% than previous state-of-the-art approaches with 4.07 higher efficiency. The solution qualities outperform human implementations by at most 2.03 in the PPA trade-off. |
| Researcher Affiliation | Academia | Chen Bai1, Jianwang Zhai2 , Yuzhe Ma3, Bei Yu1 , Martin D.F. Wong4 1The Chinese University of Hong Kong 2Beijing University of Posts and Telecommunications 3The Hong Kong University of Science and Technology (Guangzhou) 4Hong Kong Baptist University |
| Pseudocode | No | The paper describes the methodology using text and mathematical equations, but it does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is publicly available at https://github.com/baichen318/rl-explorer. |
| Open Datasets | No | We use towers, vvadd, spmv from official RISC-V tests as workloads in the DSE. |
| Dataset Splits | No | The paper mentions the use of training data for PPA model calibration but does not explicitly describe train/validation/test splits for the RL agent's DSE task. It states: 'By leveraging around 800 900 Sonic BOOM microarchitecture designs, the Kendall τ for PPA modeling results can achieve higher than 0.92.' |
| Hardware Specification | Yes | All experiments are conducted on 80 Quad Intel(R) Xeon(R) CPU E7-4820 V3 cores with a 1 TB main memory. |
| Software Dependencies | Yes | Specifically, the performance, power, and area values are obtained from Synopsys VCS M2017.03, Synopsys Prime Time PX R-2020.09-SP1, and Cadence Genus 18.12-e012 1 with 7-nm technology (Clark et al. 2016). |
| Experiment Setup | Yes | The coefficient κ in Equation (5) is set as 1, ρ in Equation (6) is 0.5, λ in Equation (7) is 0.95 and the discount factor ζ in Equation (7) is 0.99. Adam optimizer is used, and the initial learning rate is 0.001. |