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arXiv · 2312.17525

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

Sepide Saeedi·Alessandro Savino·Stefano Di Carlo
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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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Sepide Saeedi, Alessandro Savino, Stefano Di Carlo. 2023-12-29. Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach. https://doi.org/10.1109/dsn-w58399.2023.00047

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