Performance enhancement of artificial intelligence: A survey

Abstract

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.