Google AI Introduces ArchGym: An Open Source Machine Learning Gym That Connects a Wide Range of Research Algorithms to Architecture Simulators

Computer architecture research has a long history of producing simulators and tools to evaluate and influence the design of computer systems. For example, in the late 1990s, the SimpleScalar simulator was developed to allow scientists to test new microarchitecture concepts. Computer architecture research has come a long way thanks to simulations and tools like gem5, DRAMSys and many others. Since then, the discipline has progressed significantly due to the widespread availability of shared academic and corporate resources and infrastructure.

Industry and academia are increasingly focusing on optimizing machine learning (ML) in computer architecture research to meet stringent domain-specific requirements. These include ML for computer architecture, ML for TinyML acceleration, DNN accelerator data path optimization, memory controller, power consumption, security and privacy. While previous work has shown the benefits of machine learning in design optimization, there are still barriers to their adoption, such as the lack of robust and reproducible baselines, that prevent fair and objective comparisons between different methodologies. Consistent development requires a joint appreciation and assault on these obstacles.

The use of machine learning (ML) to streamline the process of exploring the design space for domain-specific architectures has become widespread. While using ML to explore the design space is tempting, doing so is fraught with difficulties:

1. Finding the best algorithm in a growing library of ML techniques is tough.
2. There is no clear-cut way to evaluate the relative performance of approaches and sample efficiency.
3. The adoption of machine learning-assisted architecture design space exploration and the production of repeatable artifacts are hampered by the absence of a unified framework for fair, reproducible, and objective comparison of various methodologies.

To address these issues, Google researchers present ArchGym, a flexible, open source gym that integrates numerous research techniques with building simulators.

Architecture Research with Machine Learning: Big Challenges

There are many hurdles in the way of studying architecture with the help of machine learning.

There is no method for systematically determining the best machine learning (ML) algorithm or hyperparameters (e.g., learning rate, warm-up steps, etc.) for a given problem in computer architecture (e.g., identifying the best solution for a DRAM controller). Design space exploration (DSE) can now use a greater variety of ML and heuristic methods, from random walks to reinforcement learning (RL). While these techniques greatly improve performance above chosen baselines, it is unclear whether this is due to the optimization algorithms used or the hyperparameters set.

Computer architecture simulators have been essential to the advancement of architecture, but there is a pressing concern to balance accuracy, efficiency, and economy during the exploration phase. Depending on the specifics of the model used (for example, cycle-accurate proxy models versus ML-based ones), simulators can provide very different performance estimates. ML-based or analytical proxy models are agile because they can ignore low-level features, but typically have a high prediction error. Additionally, commercial licenses may limit how often a simulator can be used to collect data. In summary, these trade-offs between performance and sample efficiency affect the optimization algorithm selected for design exploration.

Last but not least, the ML algorithm environment is changing rapidly and some ML algorithms rely on data to function properly. Additionally, it is essential to gain insight into the design space by visualizing DSE output in relevant artifacts, such as datasets.

Designed by ArchGym

ArchGym solves these problems by giving us a uniform way to consistently compare and contrast various ML-based search algorithms. It has two main parts:

1) The setting of the ArchGym

2) The ArchGym employee

To calculate the computational cost of running the workload given a set of architectural parameters, the environment encapsulates the cost model of the architecture and the desired workloads. The agent contains the hyperparameters and policies that drive the ML algorithm used in the search. Hyperparameters are an integral part of the algorithm for which the model is being optimized and can have a significant impact on the results. Instead, the policy specifies how the agent should choose a parameter to optimize the goal over time.

ArchGym’s standardized interface combines these two parts, and the ArchGym dataset is where all exploration information is stored. The three primary signals that make up the interface are hardware status, parameters, and metrics. These signals are the minimum required to establish a reliable line of communication between the agent and its surroundings. These signals allow the agent to monitor the health of the hardware and recommend adjusting its settings to maximize a reward (specified by the customer). The incentive is proportional to different hardware efficiency measures.

Researchers use ArchGym to empirically demonstrate that at least one combination of hyperparameters produces the same hardware performance as other ML methods, and this is true for a wide range of optimization goals and DSE situations. An incorrect conclusion about which family of ML algorithms is superior can be reached if the hyperparameter for the ML algorithm or its baseline is chosen arbitrarily. They demonstrate that various search algorithms, including random walk (RW), can find the optimal reward with proper hyperparameter tuning. However, remember that it may take a lot of work or luck to identify the optimal combination of hyperparameters.

ArchGym enables a common and extensible interface to the ML DSE architecture and is available as open source software. ArchGym also facilitates more robust baselines for computer architecture research problems and allows for fair and reproducible evaluation of various ML techniques. The researchers think it would be a huge step forward if computer architecture researchers had a place to gather where they could use machine learning to speed up their work and inspire new and creative design ideas.

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