Abstract:
With the rising prominence of RISC-V-based microprocessors in processor design, the challenge of exploring the vast and complex RISC-V microarchitecture design space has ...Show MoreMetadata
Abstract:
With the rising prominence of RISC-V-based microprocessors in processor design, the challenge of exploring the vast and complex RISC-V microarchitecture design space has become increasingly apparent. We propose the Berkeley Out-of-Order Machine Semi-Supervised Explorer (BSSE)—a novel framework leveraging the semi-supervised learning method and parallel emulation to speed up and make tradeoffs on the RISC-V microarchitecture design space exploration (DSE). BSSE constructs the initial training dataset with the microarchitecture experimental design sampling (MEDS) method and then employs the cotraining-style k-nearest neighbors (Co-KNN) model to fit the microarchitecture features to the architectural metric value space. The trained Co-KNN model assists in searching a Pareto-optimal set with parallel emulation. Finally, a distance-based method is proposed to select a designer-preferred microarchitecture from the identified Pareto-optimal set. Extensive experiments on the Berkeley Out-of-Order Machine (BOOM) show that our proposed BSSE method can search for a better Pareto-optimal set with less time consumption compared to the state-of-the-art methods and can find microarchitectures that are equivalent to or even better than the existing manually designed BOOM microarchitectures.
Published in: IEEE Transactions on Very Large Scale Integration (VLSI) Systems ( Volume: 32, Issue: 5, May 2024)
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- IEEE Keywords
- Index Terms
- Design Space ,
- Semi-supervised Learning ,
- Design Space Exploration ,
- Training Dataset ,
- Semi-supervised Methods ,
- Architectural Space ,
- Pareto Optimal Set ,
- Regression Model ,
- Deep Learning ,
- Learning Algorithms ,
- Artificial Neural Network ,
- Machine Learning Methods ,
- Active Learning ,
- Internet Of Things ,
- Time Complexity ,
- Weight Vector ,
- Radial Basis Function ,
- Kinds Of Methods ,
- Unlabeled Data ,
- Training Iterations ,
- Pareto Front ,
- Design Points ,
- Pareto Set ,
- Instruction Set Architecture ,
- Physical Layout ,
- Scale Datasets ,
- Unlabeled Set ,
- Search Algorithm ,
- Machine Learning Models ,
- Ablation Experiments
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Design Space ,
- Semi-supervised Learning ,
- Design Space Exploration ,
- Training Dataset ,
- Semi-supervised Methods ,
- Architectural Space ,
- Pareto Optimal Set ,
- Regression Model ,
- Deep Learning ,
- Learning Algorithms ,
- Artificial Neural Network ,
- Machine Learning Methods ,
- Active Learning ,
- Internet Of Things ,
- Time Complexity ,
- Weight Vector ,
- Radial Basis Function ,
- Kinds Of Methods ,
- Unlabeled Data ,
- Training Iterations ,
- Pareto Front ,
- Design Points ,
- Pareto Set ,
- Instruction Set Architecture ,
- Physical Layout ,
- Scale Datasets ,
- Unlabeled Set ,
- Search Algorithm ,
- Machine Learning Models ,
- Ablation Experiments
- Author Keywords