2018 Ieee International Symposium On Circuits And Systems Iscas

2018 IEEE International Symposium on Circuits and Systems (ISCAS)

ISCAS is the world s premier networking and exchange forum for leading researchers in the highly active fields of theory, design and implementation of circuits and systems ISCAS is the flagship conference of the IEEE Circuits and Systems Society and aim to bring together its multidisciplinary community that has a strong history of cultivating creative, proof of concept research in a diverse range of technical domains including analog and digital circuits and systems, nanotechnology, sensors, nonlinear systems, biosystems, neural systems, signal processing, and communications

2018 IEEE International Symposium on Circuits and Systems (ISCAS)

VLSI and Hardware Implementations using Modern Machine Learning Methods

Machine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods, algorithms, architectures, and frameworks designed for VLSI design. The focus is on digital, analog, and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall, the book provides practical implementations of VLSI design, IC design, and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers, professionals, and graduate students in VLSI, machine learning, electrical and electronic engineering, computer engineering, and hardware systems.