Weiqiang L. Design and Applications of Emerging Computer Systems 2024

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Weiqiang L. Design and Applications of Emerging Computer Systems 2024

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Textbook in PDF format This book provides a single-source reference to the state-of-the-art in emerging computer systems. The authors address the technological contributions and developments at various hardware levels of new systems that compute under novel operational paradigms such as stochastic, probabilistic/inexact, neuromorphic, spintronic, bio-inspired, and in-memory computing. Coverage includes the entire stack, i.e., from circuit, and architecture, up to the system level. This book includes tutorials, reviews, and surveys of current theoretical/experimental results, design methodologies, and a range of applications. Preface. References. In-Memory Computing, Neuromorphic Computing and Machine Learning. Emerging Technologies for Memory-Centric Computing. An Overview of Computation-in-Memory (CIM) Architectures. Toward Spintronics Non-volatile Computing-in-Memory Architecture. Is Neuromorphic Computing the Key to Power-Efficient Neural Networks: A Survey. Emerging Machine Learning Using Siamese and Triplet Neural Networks. An Active Storage System for Intelligent Data Analysis and Management. Error-Tolerant Techniques for Classifiers Beyond Neural Networks for Dependable Machine Learning. Stochastic Computing. Efficient Random Number Sources Based on D Flip-Flops for Stochastic Computing. Stochastic Multipliers: from Serial to Parallel. Applications of Ising Models Based on Stochastic Computing. Stochastic and Approximate Computing for Deep Learning: A Survey. Stochastic Computing Applications to Artificial Neural Networks. Characterizing Stochastic Number Generators for Accurate Stochastic Computing. Inexact/Approximate Computing. Automated Generation and Evaluation of Application-Oriented Approximate Arithmetic Circuits. Automatic Approximation of Computer Systems Through Multi-objective Optimization. Evaluation of the Functional Impact of Approximate Arithmetic Circuits on Two Application Examples. A Top-Down Design Methodology for Approximate Fast Fourier Transform (FFT) Design. Approximate Computing in Deep Learning System: Cross-Level Design and Methodology. Adaptive Approximate Accelerators with Controlled Quality Using Machine Learning. Design Wireless Communication Circuits and Systems Using Approximate Computing. Logarithmic Floating-Point Multipliers for Efficient Neural Network Training. Quantum Computing and Other Emerging Computing. Cryogenic CMOS for Quantum Computing. Quantum Computing on Memristor Crossbars. A Review of Posit Arithmetic for Energy-Efficient Computation: Methodologies, Applications, and Challenges. Designing Fault-Tolerant Digital Circuits in Quantum-Dot Cellular Automata. Ising Machines Using Parallel Spin Updating Algorithms for Solving Traveling Salesman Problems. Approximate Communication in Network-on-Chips for Training and Inference of Image Classification Models. Index