Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach
Hardware Architecture
2024-01-01 v1 Machine Learning
Performance
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.
Cite
@article{arxiv.2312.17525,
title = {Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach},
author = {Sepide Saeedi and Alessandro Savino and Stefano Di Carlo},
journal= {arXiv preprint arXiv:2312.17525},
year = {2024}
}