Angol
This book extensively examines the characteristics of modern Field Programmable Gate Arrays (FPGAs), delving into design methodologies and showcasing successful real-world applications within the realm of Artificial Intelligence (AI). It provides a comprehensive overview of the spectrum of hardware options available for AI tasks, highlighting the inherent advantages of FPGAs. Furthermore, the book delves into contemporary FPGA Integrated Circuits (ICs) while emphasizing the synergy with software support.At its core, the book revolves around the central theme of leveraging FPGA's parallel processing capabilities to meet the substantial computational demands inherent to AI applications. This involves not only comprehensive hardware implementations but also the development of tailored hardware accelerators. The textbook offers a panoramic view of the subject matter, encompassing diverse domains such as technological foundations for AI deployment, FPGA capabilities, software tools, and a wide array of hardware solutions from various suppliers. Moreover, it provides insights into research-driven implementations aimed at motivating the AI community to embrace and explore the potential of FPGA technology.This book covers the entirety of machine learning accelerators, opening pathways for the creation and advancement of intelligent applications. It transcends a singular perspective, avoiding exclusive focus on reconfigurable architectures for signal and image processing applications.For researchers seeking a profound understanding of the intricacies involved in conceiving and developing AI, deep learning, and machine learning applications using the FPGA platform, this book serves as an indispensable resource. Its audience is diverse, encompassing researchers, industry experts, scientists, and postgraduate students operating within the fields of computer engineering, electronics, and electrical engineering. It holds particular significance for those specializing in VLSI and embedded systems, FPGA technology, as well as machine learning and deep learning, among other multidisciplinary projects within these domains.
