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.

翻译：近似计算（AxC）技术通过在各类应用中牺牲精度换取性能提升，已日益普及。针对特定应用选择最优的AxC技术颇具挑战性。在设计空间探索的诸多方法中，强化学习（RL）等机器学习方法展现出良好前景。本文提出一种基于强化学习的多目标设计空间探索策略，用于寻找能平衡精度损失与功耗、计算时间缩减的应用近似版本。实验结果表明，在若干基准测试中，该方法实现了精度损失与功耗、计算时间缩减之间的良好权衡。