An Open-Source Gymnasium for Machine Learning Assisted Computer Architecture Design

Computer architecture is a complex and challenging field. The design of a computer architecture must take into account a wide range of factors, including performance, power consumption, and cost. In recent years, machine learning (ML) has been increasingly used to improve the design of computer architectures.

ML can be used to automate many of the tasks involved in computer architecture design, such as:

• Identifying the most important design parameters: ML can be used to analyze large datasets of computer architectures and identify the parameters that have the biggest impact on performance, power consumption, and cost.
• Generating new design ideas: ML can be used to generate new design ideas that would not be possible to find through manual search.
• Optimizing the design of a computer architecture: ML can be used to optimize the design of a computer architecture for a specific set of requirements.

However, there are a number of challenges in using ML for computer architecture design, including:

• Choosing the right ML algorithm: There are a wide variety of ML algorithms available, and it can be difficult to know which one is the best fit for a particular problem.
• Tuning the hyperparameters: The performance of an ML algorithm can be sensitive to the values of its hyperparameters. Finding the right hyperparameters can be a time-consuming and challenging process.
• Building a dataset: In order to train an ML algorithm, it is necessary to have a dataset of examples. This dataset can be difficult and time-consuming to build, especially for complex problems.

ArchGym is an open-source framework that addresses these challenges. ArchGym provides a unified framework for evaluating different ML-based search algorithms for architecture design. It also includes a number of features that make it easier to build and use ML-based architecture design tools, such as:

• A standardized interface for connecting ML algorithms to architecture simulators
• A mechanism for logging data from architecture simulations
• A dataset of example designs

ArchGym is a valuable resource for researchers and practitioners who are interested in using ML to improve the design of computer architectures. It makes it easier to compare different ML algorithms, to build datasets, and to tune the hyperparameters of ML algorithms.

In a recent study, the authors of ArchGym showed that with sufficient hyperparameter tuning, different search algorithms, even random walk (RW), are able to identify the best possible reward. However, they also found that the choice of hyperparameters can have a significant impact on the performance of an ML algorithm.

The authors of ArchGym believe that the creation of a gymnasium-type environment for computer architecture research would be a significant step forward in the field. They invite the computer architecture community as well as the ML community to actively participate in the development of ArchGym.

Benefits of ArchGym

ArchGym offers a number of benefits for researchers and practitioners who are interested in using ML to improve the design of computer architectures. These benefits include:

• Ease of use: ArchGym is easy to use, even for researchers who are not familiar with ML.
• Flexibility: ArchGym is flexible enough to be used for a wide variety of computer architecture design problems.
• Reproducibility: ArchGym makes it easy to reproduce the results of experiments, which is important for scientific research.
• Community support: ArchGym is a community-driven project, which means that there is a large community of users who can provide support and help with troubleshooting.

Conclusion

ArchGym is an important tool for researchers and practitioners who are interested in using ML to improve the design of computer architectures. It makes it easier to compare different ML algorithms, to build datasets, and to tune the hyperparameters of ML algorithms. The authors of ArchGym believe that the creation of a gymnasium-type environment for computer architecture research would be a significant step forward in the field.

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