MoDSE: A High-Accurate Multiobjective Design Space Exploration Framework for CPU Microarchitectures

To accelerate time-consuming multiobjective design space exploration of CPU microarchitecture, previous work trains prediction models using a set of performance metrics derived from a few simulations, then predicts the rest. Unfortunately, the low accuracy of models limits the exploration effect, an...

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Bibliographic Details
Published in:IEEE transactions on computer-aided design of integrated circuits and systems 2024-05, Vol.43 (5), p.1525-1537
Main Authors: Wang, Duo, Yan, Mingyu, Teng, Yihan, Han, Dengke, Liu, Xin, Li, Wenming, Ye, Xiaochun, Fan, Dongrui
Format: Article
Language:English
Subjects:
Algorithms
Central Processing Unit
CPU microarchitecture
design space exploration (DSE)
Ensemble learning
Measurement
Microarchitecture
Model accuracy
multiobjective exploration
Multiple objective analysis
Optimization
Pareto hypervolume
Pareto optimization
Pareto optimum
Performance measurement
Prediction algorithms
prediction model
Prediction models
Predictive models
Space exploration
Tradeoffs
Citations: Items that this one cites
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Summary:To accelerate time-consuming multiobjective design space exploration of CPU microarchitecture, previous work trains prediction models using a set of performance metrics derived from a few simulations, then predicts the rest. Unfortunately, the low accuracy of models limits the exploration effect, and how to achieve a good tradeoff between multiple objectives while reducing exploration time is challenging. In this article, we investigate various prediction models and find out the most accurate basic model. We enhance the model by ensemble learning and generate Pareto-rank-based sample weights to improve prediction accuracy. A hypervolume-improvement-based optimization method to tradeoff between multiple objectives is proposed together with a uniformity-aware selection algorithm to jump out of the local optimum. Furthermore, the exploration time is reduced owing to a proposed Pareto-aware filter algorithm. Experiments demonstrate that our open-source framework can reduce the distance to the Pareto-optimal set by 39% compared with the state-of-the-art framework.
ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2023.3340059