ArchGym: A Flexible and Open-Source Framework for ML-Based Computer Architecture Design Exploration

Research into computer architecture has a long history of producing simulators and tools for assessing and influencing computer system design. In the late 1990s, the SimpleScalar simulator was developed to test new microarchitecture concepts. Simulations and tools like gem5 and DRAMSys have contributed to the advancement of computer architecture research.

Machine learning optimization in computer architecture has become a focus in both industry and academia. It is used to meet specific requirements, such as ML for computer architecture, ML for TinyML acceleration, and DNN accelerator datapath optimization, among others. However, the adoption of ML in design optimization faces obstacles, including the lack of robust and reproducible baselines for fair comparison.

ArchGym, an open-source gym, addresses these challenges by providing a flexible framework for integrating various search techniques with building simulators. It allows for consistent and objective comparison across different methodologies. ArchGym consists of two parts: the ArchGym environment, which encapsulates the architecture cost model and desired workload, and the ArchGym agent, which contains hyperparameters and policies for directing the ML algorithm used in the search.

ArchGym enables researchers to compare ML-based search algorithms effectively. It provides a standardized interface and stores all exploration information in the ArchGym Dataset. The interface includes signals for hardware status, parameters, and metrics, allowing the agent to monitor the hardware’s health and optimize settings for maximum reward. The researchers demonstrate that suitable hyperparameter adjustment can yield optimal rewards across various optimization targets and DSE situations.

ArchGym is available as open-source software and offers a common interface for ML architecture design space exploration. It facilitates robust baselines and reproducible evaluation of ML techniques in computer architecture research. This tool can significantly accelerate research and inspire new design ideas.

To learn more about ArchGym, you can visit the Google Blog, read the paper, or check out the Github Link. Don’t forget to join the ML SubReddit, Discord Channel, and Email Newsletter for the latest AI research news and updates. If you have any questions or missed anything in the article, feel free to email

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Dhanshree Shenwai is a Computer Science Engineer with experience in FinTech companies. She has a keen interest in AI applications and explores advancements in technology to make life easier for everyone.

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