Design Space Exploration of Approximate Computing Techniques with a
  Reinforcement Learning Approach
Sepide Saeedi Alessandro Savino Stefano Di Carlo
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.