Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2312.17525v1
• Date: Fri, 29 Dec 2023 09:10:40 GMT
• Title: Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach
• Authors: Sepide Saeedi, Alessandro Savino, Stefano Di Carlo
• Abstract summary: We propose an RL-based strategy to find approximate versions of an 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.
• Score: 49.42371633618761
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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