It has been a long time that computerarchitecture and systems are optimized for efficient execution of machinelearning (ML) models. Now, it is time to reconsider the relationship between ML and systems and let ML tr...
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It has been a long time that computerarchitecture and systems are optimized for efficient execution of machinelearning (ML) models. Now, it is time to reconsider the relationship between ML and systems and let ML transform the way that computerarchitecture and systems are designed. This embraces a twofold meaning: improvement of designers' productivity and completion of the virtuous cycle. In this article, we present a comprehensive review of the work that applies ML for computerarchitecture and system design. First, we perform a high-level taxonomy by considering the typical role that ML techniques take in architecture/system design, i.e., either for fast predictive modeling or as the design methodology. Then, we summarize the common problems in computerarchitecture/system design that can be solved by ML techniques and the typical ML techniques employed to resolve each of them. In addition to emphasis on computerarchitecture in a narrow sense, we adopt the concept that data centers can be recognized as warehouse-scale computers;sketchy discussions are provided in adjacent computer systems, such as code generation and compiler;we also give attention to how ML techniques can aid and transform design automation. We further provide a future vision of opportunities and potential directions and envision that applying ML for computerarchitecture and systems would thrive in the community.
machinelearning (ML) has become a prevalent approach to tame the complexity of design space exploration for domain-specific architectures. While appealing, using ML for design space exploration poses several challeng...
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ISBN:
(纸本)9798400700958
machinelearning (ML) has become a prevalent approach to tame the complexity of design space exploration for domain-specific architectures. While appealing, using ML for design space exploration poses several challenges. First, it is not straightforward to identify the most suitable algorithm from an ever-increasing pool of ML methods. Second, assessing the trade-offs between performance and sample efficiency across these methods is inconclusive. Finally, the lack of a holistic framework for fair, reproducible, and objective comparison across these methods hinders the progress of adopting ML-aided architecture design space exploration and impedes creating repeatable artifacts. To mitigate these challenges, we introduce ArchGym, an open-source gymnasium and easy-to-extend framework that connects a diverse range of search algorithms to architecture simulators. To demonstrate its utility, we evaluate ArchGym across multiple vanilla and domain-specific search algorithms in the design of a custom memory controller, deep neural network accelerators, and a custom SoC for AR/VR workloads, collectively encompassing over 21K experiments. The results suggest that with an unlimited number of samples, ML algorithms are equally favorable to meet the user-defined target specification if its hyperparameters are tuned thoroughly;no one solution is necessarily better than another (e.g., reinforcement learning vs. Bayesian methods). We coin the term "hyperparameter lottery" to describe the relatively probable chance for a search algorithm to find an optimal design provided meticulously selected hyperparameters. Additionally, the ease of data collection and aggregation in ArchGym facilitates research in ML-aided architecture design space exploration. As a case study, we show this advantage by developing a proxy cost model with an RMSE of 0.61% that offers a 2,000-fold reduction in simulation time. Code and data for ArchGym is available at https://***/ArchGym.
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