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全国の多くの大学のカリキュラムやシラバスに合った、「コンピュータアーキテクチャ」の新たな定番テキスト 電気・電子・情報系学科の専門課目である「コンピュータアーキテクチャ」「計算機工学（概論）」の講義用教科書。情報の表現方法、論理演算を実現する方法を学ぶとともに、演算回路、レジスタファイル、メモリなどのハードウェアの基本部品に関する知識、またそれらのC言語との関係について解説。また、パイプライン処理方式、記憶階層の考え方および実現方法、OSの基本機能、プロセスとその管理の実際、代表的なI/O装置の仕組みなどをわかりやすく解説するものです。全体は1コマの講義の回数に合った15章構成をとっており、各章末とも理解度をチェックする演習問題を配しています。

The direct current flux parametron using Josephson junction and based on the principle of parametron seems to be the answer for the search of new technology for the future needs of computer power. The combination of its very high switching speed and highly integrable Josephson junction with the established parametron computer technology accounts for its high performance. This book presents the new type of Josephson computer technology which is fundamentally different from the conventional approach. In this approach, a logic state is represented by a flux polarity whereas the conventional approach uses a voltage state to represent a logic state.

This book concerns a Josephson device for supercomputers which has extremely low heat dissipation (about 106 times less than semiconductor devices and 103 times less than voltage-based Josephson devices). In the previous book on Quantum Flux Parametrons (QFPs), DC Flux Parametron, the basic device operation are described. This book deals in much greater depth on the problems which are faced by the QFP. The device characteristics are worked out in detail showing clearly the analysis methods used. A new logic gate using the QFP is described with respect to its basic scheme, operation, and ways for forming logic circuits. The problems faced by the basic QFP are much reduced in the new logic gate. As the QFP operates near the Heisenberg and Boltzmann limits for computing devices, we also show the relationship between speed and stability. The book contains the latest analytical results on QFPs.The material presented in the book can be understood with very little mathematical training or knowledge about superconducting physics. It is also self-contained and does not require reading of other material. Most of the device characteristics can be reproduced from the equations given using simple programs. A circuit simulator is not needed except for high speeds when transient behavior becomes important.

Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how well neural networks scale - that is, how fast the computation time and number of neurons grow as the problem size increases. It surveys recent research in circuit complexity (a robust branch of theoretical computer science) and applies this work to a theoretical understanding of the problem of scalability. Most research in neural networks focuses on learning, yet it is important to understand the physical limitations of the network before the resources needed to solve a certain problem can be calculated. One of the aims of this book is to compare the complexity of neural networks and the complexity of conventional computers, looking at the computational ability and resources (neurons and time) that are a necessary part of the foundations of neural network learning. Circuit Complexity and Neural Networks contains a significant amount of background material on conventional complexity theory that will enable neural network scientists to learn about how complexity theory applies to their discipline, and allow complexity theorists to see how their discipline applies to neural networks.