Performance enhancement of artificial intelligence: A survey

• Published in: Journal of network and computer applications Vol. 232; p. 104034
• Main Authors: Krichen, Moez, Abdalzaher, Mohamed S.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2024
• Subjects: Artificial intelligence; Data processing approaches; Machine learning; Optimization techniques; Performance evaluation; Performance evaluation; Optimization techniques; Data processing approaches; Artificial intelligence; Machine learning
• ISSN: 1084-8045
• DOI: 10.1016/j.jnca.2024.104034

The advent of machine learning (ML) and Artificial intelligence (AI) has brought about a significant transformation across multiple industries, as it has facilitated the automation of jobs, extraction of valuable insights from extensive datasets, and facilitation of sophisticated decision-making processes. Nevertheless, optimizing efficiency has become a critical research field due to AI systems’ increasing complexity and resource requirements. This paper provides an extensive examination of several techniques and methodologies aimed at improving the efficiency of ML and artificial intelligence. In this study, we investigate many areas of research about AI. These areas include algorithmic improvements, hardware acceleration techniques, data pretreatment methods, model compression approaches, distributed computing frameworks, energy-efficient strategies, fundamental concepts related to AI, AI efficiency evaluation, and formal methodologies. Furthermore, we engage in an examination of the obstacles and prospective avenues in this particular domain. This paper offers a deep analysis of many subjects to equip researchers and practitioners with sufficient strategies to enhance efficiency within ML and AI systems. More particularly, the paper provides an extensive analysis of efficiency-enhancing techniques across multiple dimensions: algorithmic advancements, hardware acceleration, data processing, model compression, distributed computing, and energy consumption. •The study comprehensively reviews ML/AI methods, including algorithms, hardware, and practical applications.•The study provides deep insights into improving ML/AI efficiency for experts.•The study offers guidance to optimize AI performance and resource utilization with theoretical and practical connections.•The study prepares readers for future ML/AI challenges and advancements.•The study fosters innovation by advancing ML/AI through analysis, insights, and perspectives.