Google AI has introduced ArchGym, an open-source gymnasium for machine learning that connects a diverse range of search algorithms to architecture simulators. This innovation aims to simplify the process of exploring design space for domain-specific architectures.
The field of computer architecture has a long history of developing simulators and tools for assessing and influencing computer system design. Over the years, these advancements have significantly contributed to the discipline’s progress. However, there are still obstacles to overcome for the adoption of machine learning (ML) in design optimization, such as the lack of robust and reproducible baselines for fair comparisons across methodologies.
With ArchGym, Google researchers seek to address these challenges by providing a flexible and open-source gym that integrates various search techniques with building simulators. By doing so, this platform allows for the systematic exploration of design space in computer architecture through ML algorithms.
One major obstacle in studying architecture using machine learning is the lack of a method to determine the best ML algorithm and hyperparameters for a given problem. ArchGym aims to overcome this challenge by providing a standardized interface that allows for consistent comparison and contrast of ML-based search algorithms. This enables researchers to evaluate different ML techniques in a fair and reproducible manner.
Another challenge lies in balancing the precision, efficiency, and economy of computer architecture simulators during the exploration phase. Depending on the specific model used, simulators can provide different performance estimates. ArchGym addresses this concern by incorporating proxy models that are either analytical or ML-based, providing a more agile approach to simulating architecture. Additionally, ArchGym facilitates the visualization of design space exploration output in relevant artifacts, such as datasets.
ArchGym consists of two primary parts: the environment and the agent. The environment encapsulates the architecture’s cost model and desired workload, while the agent contains the hyperparameters and policies that direct the ML algorithm used in the search. These components are unified through the standardized interface, which allows for the storage of all exploration information in the ArchGym Dataset.
With empirical evidence, researchers demonstrate that at least one combination of hyperparameters can yield the same hardware performance as other ML methods across various optimization targets and design space exploration situations. This highlights the importance of selecting suitable hyperparameters when evaluating different ML algorithms.
Overall, ArchGym provides a common and extensible interface for ML architecture design space exploration. As an open-source software, it also facilitates the development of robust baselines for computer architecture research problems and enables fair and reproducible evaluation of ML techniques.
In conclusion, Google AI’s introduction of ArchGym serves as a valuable resource for researchers in the field of computer architecture. This platform enables the utilization of machine learning to streamline work processes and spark innovative design ideas. By addressing the challenges in ML-based design space exploration, ArchGym contributes to the advancement of computer architecture research.
