Springer Emerging Technology and Architecture for Big-data Analytics

Product Description This book describes the current state of the art in big-data analytics, from a technology and hardware architecture perspective. The presentation is designed to be accessible to a broad audience, with general knowledge of hardware design and some interest in big-data analytics. Coverage includes emerging technology and devices for data-analytics, circuit design for data-analytics, and architecture and algorithms to support data-analytics. Readers will benefit from the realistic context used by the authors, which demonstrates what works, what doesn’t work, and what are the fundamental problems, solutions, upcoming challenges and opportunities. Provides a single-source reference to hardware architectures for big-data analytics; Covers various levels of big-data analytics hardware design abstraction and flow, from device, to circuits and systems; Demonstrates how non-volatile memory (NVM) based hardware platforms can be a viable solution to existing challenges in hardware architecture for big-data analytics. From the Back Cover This book describes the current state of the art in big-data analytics, from a technology and hardware architecture perspective. The presentation is designed to be accessible to a broad audience, with general knowledge of hardware design and some interest in big-data analytics. Coverage includes emerging technology and devices for data-analytics, circuit design for data-analytics, and architecture and algorithms to support data-analytics. Readers will benefit from the realistic context used by the authors, which demonstrates what works, what doesn’t work, and what are the fundamental problems, solutions, upcoming challenges and opportunities. Provides a single-source reference to hardware architectures for big-data analytics; Covers various levels of big-data analytics hardware design abstraction and flow, from device, to circuits and systems; Demonstrates how non-volatile memory (NVM) based hardware platforms can be a viable solution to existing challenges in hardware architecture for big-data analytics. About the Author Anupam Chattopadhyay received his B.E. degree from Jadavpur University, India in 2000. He received his MSc. from ALaRI, Switzerland and PhD from RWTH Aachen in 2002 and 2008 respectively. From 2008 to 2009, he worked as a Member of Consulting Staff in CoWare R&D, Noida, India. From 2010 to 2014, he led the MPSoC Architectures Research Group in RWTH Aachen, Germany as a Junior Professor. Since September, 2014, he is appointed as an assistant Professor in SCE, NTU. During his PhD, he worked on automatic RTL generation from the architecture description language LISA, which was commercialized later by a leading EDA vendor. He developed several high-level optimizations and verification flow for embedded processors. In his doctoral thesis, he proposed a language-based modeling, exploration and implementation framework for partially re-configurable processors. Together with his doctoral students, he proposed domain-specific high-level synthesis for cryptography, high-level reliability estimation flows, generalization of classic linear algebra kernels and a novel multi-layered coarse-grained reconfigurable architecture. In these areas, he published as a (co)-author over 80 conference/ journal papers, several book-chapters and a book. Anupam served in several TPCs of top conferences, regularly reviews journal/ conference articles and presented multiple invited seminars/tutorials in prestigious venues. He is a member of ACM and a senior member of IEEE. Chang Chip Hong received his B.Eng. (Hons) from National University of Singapore in 1989, and his M.Eng. and Ph.D. from the School of Electrical and Electronic Engineering of Nanyang Technological University, Singapore in 1993 and 1998, respectively. Since 1999, he has been with the School of Electrical and Electronic Engineering, Nanyang Technological University where he is currently an As