Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach
This work addresses the problem of efficiently exploring design spaces for Approximate Computing, which is incremental as it applies an existing RL method to this specific domain.
The paper tackled the challenge of selecting optimal Approximate Computing techniques for balancing accuracy with performance gains by proposing a Reinforcement Learning-based multi-objective Design Space Exploration strategy, achieving a good trade-off between accuracy degradation and reduced power and computation time in benchmarks.
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.