  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.
