A Genetic-algorithm-based Approach to the Design of DCT Hardware Accelerators

Author:

Barbareschi Mario1ORCID,Barone Salvatore1ORCID,Bosio Alberto2ORCID,Han Jie3ORCID,Traiola Marcello4ORCID

Affiliation:

1. Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Italy

2. Univ Lyon, ECL, INSA Lyon, CNRS, UCBL, CPE Lyon, INL, UMR5270, France

3. Department of Electrical and Computer Engineering, University of Alberta, Canada

4. University of Rennes, Inria, CNRS, IRISA, UMR 6074, France

Abstract

As modern applications demand an unprecedented level of computational resources, traditional computing system design paradigms are no longer adequate to guarantee significant performance enhancement at an affordable cost. Approximate Computing (AxC) has been introduced as a potential candidate to achieve better computational performances by relaxing non-critical functional system specifications. In this article, we propose a systematic and high-abstraction-level approach allowing the automatic generation of near Pareto-optimal approximate configurations for a Discrete Cosine Transform (DCT) hardware accelerator. We obtain the approximate variants by using approximate operations, having configurable approximation degree, rather than full-precise ones. We use a genetic searching algorithm to find the appropriate tuning of the approximation degree, leading to optimal tradeoffs between accuracy and gains. Finally, to evaluate the actual HW gains, we synthesize non-dominated approximate DCT variants for two different target technologies, namely, Field Programmable Gate Arrays (FPGAs) and Application Specific Integrated Circuits (ASICs). Experimental results show that the proposed approach allows performing a meaningful exploration of the design space to find the best tradeoffs in a reasonable time. Indeed, compared to the state-of-the-art work on approximate DCT, the proposed approach allows an 18% average energy improvement while providing at the same time image quality improvement.

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Software

Reference43 articles.

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