Hardware Technologies for Artificial Intelligence

While current GPUs have high parallel processing capabilities suitable for computations on large datasets, their power consumption is approaching its limit and requires further development. Additionally, edge computing is becoming increasingly important alongside cloud computing. Amid these significant technological trends, this book provides readers with important insights into next-generation computing, namely (1) neural network (artificial intelligence) LSIs and their low power and high performance, (2) hardware design technology for combinatorial optimization problems and Ising machines, and (3) semiconductor memory and data-centric computing. Kawahara first describes the basics of LSI design and neural networks before then describing their large-scale integration, power efficiency and performance enhancements. He then also explains hardware design techniques for Ising machines, offers case studies of fully coupled Ising machine LSI. Last, he discusses the basics of semiconductor memory, near/in-memory AI computing, and then examines the future prospects. Readers will be able to apply this knowledge to the design and manufacture of such devices to overcome the limitations of current hardware and computational methods, driving future advancements in artificial intelligence and optimization.
This is a valuable reference for students, engineers and researchers alike in this field. As it begins with the basics, it enables all readers to follow the direction of next-generation computing and its important technical content without the need for prior knowledge or reference to other books.

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