Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910283540201472 |
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| author | Saeedi, Sepide Savino, Alessandro Di Carlo, Stefano |
| author_facet | Saeedi, Sepide Savino, Alessandro Di Carlo, Stefano |
| contents | Approximate Computing (AxC) techniques have become increasingly popular in trading off accuracy for performance gains in various applications. Selecting the best AxC techniques for a given application is challenging. Among proposed approaches for exploring the design space, Machine Learning approaches such as Reinforcement Learning (RL) show promising results. In this paper, we proposed an RL-based multi-objective Design Space Exploration strategy to find the approximate versions of the application that balance accuracy degradation and power and computation time reduction. Our experimental results show a good trade-off between accuracy degradation and decreased power and computation time for some benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17525 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach Saeedi, Sepide Savino, Alessandro Di Carlo, Stefano Hardware Architecture Machine Learning Performance C.1 Approximate Computing (AxC) techniques have become increasingly popular in trading off accuracy for performance gains in various applications. Selecting the best AxC techniques for a given application is challenging. Among proposed approaches for exploring the design space, Machine Learning approaches such as Reinforcement Learning (RL) show promising results. In this paper, we proposed an RL-based multi-objective Design Space Exploration strategy to find the approximate versions of the application that balance accuracy degradation and power and computation time reduction. Our experimental results show a good trade-off between accuracy degradation and decreased power and computation time for some benchmarks. |
| title | Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach |
| topic | Hardware Architecture Machine Learning Performance C.1 |
| url | https://arxiv.org/abs/2312.17525 |