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.

Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach / Saeedi, Sepide; Savino, Alessandro; Di Carlo, Stefano. - ELETTRONICO. - (2023), pp. 167-170. (Intervento presentato al convegno 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) tenutosi a Porto (PRT) nel 27-30 June 2023) [10.1109/DSN-W58399.2023.00047].

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

Saeedi, Sepide;Savino, Alessandro;Di Carlo, Stefano
2023

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.
2023
979-8-3503-2543-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2981399