Study on CPU and FPGA in Artificial Intelligence and Machine Learning

This article provides a comprehensive examination of the roles played by Central Processing Units (CPUs) and Field-Programmable Gate Arrays (FPGAs) in the realms of Artificial Intelligence (AI) and Machine Learning (ML), with a particular emphasis on the superiorities of FPGA over CPU in certain aspects. It is observed that CPUs, while versatile, fall short in aspects such as power efficiency, handling the intricacies of deep learning models, and performing large-scale matrix computations. FPGAs, on the other hand, are lauded for their remarkable flexibility, low latency, superior energy efficiency, and inherent parallelism—qualities that are immensely beneficial in AI and ML applications. The article delves into the real-world applications of both CPUs and FPGAs, showcasing specific scenarios where each excels. For instance, the low latency advantage of FPGAs is crucial in image processing tasks, where quick, efficient processing can be pivotal. In contrast, CPUs are better suited for smaller-scale or general-purpose computing tasks where flexibility and ease of programming are key. This contrast highlights the nuanced roles that these technologies play in the broader context of AI and ML. Further, the article contrasts CPUs and FPGAs in terms of architecture, design philosophy, and suitability for various AI and ML workloads. While CPUs offer a general-purpose solution with a well-established ecosystem, FPGAs provide a customizable platform that can be tailored to specific needs, offering a more efficient solution for certain types of computational tasks in AI and ML. This comparison not only sheds light on the inherent strengths and weaknesses of each technology but also underscores the importance of choosing the right tool for specific AI and ML applications, thereby optimizing performance, efficiency, and power consumption.