Published June 30, 2025 | Version CC-BY-NC-ND 4.0
Journal article Open

Machine Learning in Approximate Computing Applications: A Comprehensive Review of Recent Research and Accomplishments in Energy-Efficient Computing

Creators

  • 1. Research Scholar, Department of Electronics & Communication Engineering, JNTUK, Kakinada (Andhra Pradesh), India.

Contributors

Contact person:

  • 1. Research Scholar, Department of Electronics & Communication Engineering, JNTUK, Kakinada (Andhra Pradesh), India.
  • 2. Department of Electronics & Communication Engineering, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru (Andhra Pradesh), India.
  • 3. Department of Electronics & Communication Engineering, JNTUK, Kakinada (Andhra Pradesh), India.

Description

Abstract: Recently, approximate computing has become a wellknown computer outlook. It is a broad field with new research paths emerging daily. Approximate computing systems enhance energy efficiency and computational speed at the expense of precision in output. From a computational standpoint, this paper offers a concise and thorough overview of recent research areas and accomplishments in energy-efficient computing. We classify and analyse the machine learning techniques used in approximate computing applications. Approximate computing is used at the software, circuit, and hardware levels. Machine learning (ML) methods are crucial in various approximate computing applications, enabling performance improvements at multiple levels. The scope of the systematic literature review encompasses an in-depth examination of the most prominent machine learning (ML) trending techniques in approximate computing applications. This paper also addresses recent breakthroughs in approximate computing hardware, software, and approximate data communication.

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Additional details

Identifiers

DOI
10.35940/ijeat.F4236.14050625
EISSN
2249-8958

Dates

Accepted
2025-06-15
Manuscript received on 21 June 2023 | First Revised Manuscript received on 20 December 2024 | Second Revised Manuscript received on 16 May 2025 | Manuscript Accepted on 15 June 2025 | Manuscript published on 30 June 2025.

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