Weiqiang L. Design and Applications of Emerging Computer Systems 2024

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