Analysis of the Designs and Applications of AI Chip

Abstract

The rapid evolution of deep learning model architectures and the increasing scale of model parameters have imposed heightened demands on deep learning training, inference, and deployment, leading to the swift advancement and unprecedented prosperity of AI chips. Therefore, this study sets out to analyze the designs and applications of AI chips by considering their unique requirements compared to conventional chips, and by combining software and hardware aspects. The paper delineates the classification of common AI chips along with their distinct design strategies and optimization algorithms. It commences with the fundamental hardware design of AI chips, elucidating the basic design process and addressing the specialized demands of AI computation, particularly data parallelism and storage optimization. Subsequently, transitioning to the manufacturing process, it examines how current AI chips circumvent fabrication bottlenecks and achieve significant breakthroughs in architecture and performance through chip stacking techniques. The paper then bridges hardware and software through the AI compiler, expounding on model optimization approaches, e.g., quantization and pruning, completing the comprehensive journey from AI chip design to deployment. It identifies current developmental challenges in the AI chip realm and provides a glimpse into future prospects. Through a holistic perspective spanning design, manufacturing, algorithms, and applications of AI chips, this paper offers insights that steer upcoming innovations and practical implementations in artificial intelligence, paving the way for a dynamic future in AI chip development.