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.

中文翻译：

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人工智能的性能增强：一项调查


机器学习 （ML） 和人工智能 （AI） 的出现为多个行业带来了重大转变，因为它促进了工作自动化、从广泛的数据集中提取有价值的见解以及促进复杂的决策过程。然而，由于 AI 系统的复杂性和资源需求不断增加，优化效率已成为一个关键的研究领域。本文对旨在提高 ML 和人工智能效率的几种技术和方法进行了广泛的研究。在这项研究中，我们调查了有关 AI 的许多研究领域。这些领域包括算法改进、硬件加速技术、数据预处理方法、模型压缩方法、分布式计算框架、节能策略、与 AI 相关的基本概念、AI 效率评估和正式方法。此外，我们还研究了这一特定领域的障碍和潜在途径。本白皮书对许多主题进行了深入分析，为研究人员和从业者提供了足够的策略来提高 ML 和 AI 系统的效率。更具体地说，该论文从多个维度对效率增强技术进行了广泛的分析：算法进步、硬件加速、数据处理、模型压缩、分布式计算和能源消耗。