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