Hao Yu

Leibin Ni

Yuhao Wang

Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main bottleneck is from memory, we aim to develop an energy-efficient in-memory computing platform in this book. First, the models of spin-transfer torque magnetic tunnel junction and racetrack memory are presented. Next, we show that the spintronics could be a candidate for future data-oriented computing for storage, logic, and interconnect. As a result, by utilizing spintronics, in-memory-based computing has been applied for data encryption and machine learning. The implementations of in-memory AES, Simon cipher, as well as interconnect are explained in details. In addition, in-memory-based machine learning and face recognition are also illustrated in this book.

Non-Volatile In-Memory Computing by Spintronics

Mei Yan

Xiwei Huang

A thorough examination of lab-on-a-chip circuit-level operations to improve system performance A rapidly aging population demands rapid, cost-effective, flexible, personalized diagnostics. Existing systems tend to fall short in one or more capacities, making the development of alternatives a priority. CMOS Integrated Lab-on-a-Chip System for Personalized Biomedical Diagnosis provides insight toward the solution, with a comprehensive, multidisciplinary reference to the next wave of personalized medicine technology. A standard complementary metal oxide semiconductor (CMOS) fabrication technology allows mass-production of large-array, miniaturized CMOS-integrated sensors from multi-modal domains with smart on-chip processing capability. This book provides an in-depth examination of the design and mechanics considerations that make this technology a promising platform for microfluidics, micro-electro-mechanical systems, electronics, and electromagnetics. From CMOS fundamentals to end-user applications, all aspects of CMOS sensors are covered, with frequent diagrams and illustrations that clarify complex structures and processes. Detailed yet concise, and designed to help students and engineers develop smaller, cheaper, smarter lab-on-a-chip systems, this invaluable reference: Provides clarity and insight on the design of lab-on-a-chip personalized biomedical sensors and systems Features concise analyses of the integration of microfluidics and micro-electro-mechanical systems Highlights the use of compressive sensing, super-resolution, and machine learning through the use of smart SoC processing Discusses recent advances in complementary metal oxide semiconductor-integrated lab-on-a-chip systems Includes guidance on DNA sequencing and cell counting applications using dual-mode chemical/optical and energy harvesting sensors The conventional reliance on the microscope, flow cytometry, and DNA sequencing leaves diagnosticians tied to bulky, expensive equipment with a central problem of scale. Lab-on-a-chip technology eliminates these constraints while improving accuracy and flexibility, ushering in a new era of medicine. This book is an essential reference for students, researchers, and engineers working in diagnostic circuitry and microsystems.

CMOS Integrated Lab-on-a-chip System for Personalized Biomedical Diagnosis

Peng Li

Haoran Ji

Yang Mi

Yue Zhou

Nian Liu

Flexible and Active Distribution Networks

Ruijing Shen

Sheldon X.-D. Tan

Since process variation and chip performance uncertainties have become more pronounced as technologies scale down into the nanometer regime, accurate and efficient modeling or characterization of variations from the device to the architecture level have become imperative for the successful design of VLSI chips. This book provides readers with tools for variation-aware design methodologies and computer-aided design (CAD) of VLSI systems, in the presence of process variations at the nanometer scale. It presents the latest developments for modeling and analysis, with a focus on statistical interconnect modeling, statistical parasitic extractions, statistical full-chip leakage and dynamic power analysis considering spatial correlations, statistical analysis and modeling for large global interconnects and analog/mixed-signal circuits. Provides readers with timely, systematic and comprehensive treatments of statistical modeling and analysis of VLSI systems with a focus on interconnects, on-chip power grids and clock networks, and analog/mixed-signal circuits; Helps chip designers understand the potential and limitations of their design tools, improving their design productivity; Presents analysis of each algorithm with practical applications in the context of real circuit design; Includes numerical examples for the quantitative analysis and evaluation of algorithms presented. Provides readers with timely, systematic and comprehensive treatments of statistical modeling and analysis of VLSI systems with a focus on interconnects, on-chip power grids and clock networks, and analog/mixed-signal circuits; Helps chip designers understand the potential and limitations of their design tools, improving their design productivity; Presents analysis of each algorithm with practical applications in the context of real circuit design; Includes numerical examples for the quantitative analysis and evaluation of algorithms presented.

Statistical Performance Analysis and Modeling Techniques for Nanometer VLSI Designs

Ding-Geng Chen

Jiahua Chen

Xuewen Lu

Grace Y. Yi

This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a full chapter for this book in order to disseminate the findings and promote further research collaborations in this area. This timely book offers new methods that impact advanced statistical model development in big-data sciences.

Advanced Statistical Methods in Data Science

Hantao Huang

This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.

Compact and Fast Machine Learning Accelerator for IoT Devices

Wai Keung Li

David A. Stanford

This volume reviews and summarizes some of A. I. McLeod's significant contributions to time series analysis. It also contains original contributions to the field and to related areas by participants of the festschrift held in June 2014 and friends of Dr. McLeod. Covering a diverse range of state-of-the-art topics, this volume well balances applied and theoretical research across fourteen contributions by experts in the field. It will be of interest to researchers and practitioners in time series, econometricians, and graduate students in time series or econometrics, as well as environmental statisticians, data scientists, statisticians interested in graphical models, and researchers in quantitative risk management.

The A. Ian McLeod Festschrift

Advances in Time Series Methods and Applications

Hao Yu

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