# Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2312.17525 · HTML: https://mlsanity.com/paper/2312.17525/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 29 Dec 2023.

## Builds on (most cited first, 2 of 2)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Approximate FPGA-based LSTMs under Computation Time Constraints](https://mlsanity.com/paper/1801.02190/) (2018)

## Built on by (most cited first, 2 of 2)

- [SpikeExplorer: hardware-oriented Design Space Exploration for Spiking Neural Networks on FPGA](https://mlsanity.com/paper/2404.03714/) (2024)
- [HAWX: A Hardware-Aware FrameWork for Fast and Scalable ApproXimation of DNNs](https://mlsanity.com/paper/2602.16336/) (2026)

## Abstract (arXiv)

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.