Biography

Sandeep Saini received his B.Tech. degree in Electronics and Communication Engineering from the International Institute of Information Technology, Hyderabad, India, in 2008. He completed his M.S. from the same institute in 2010. He earned his Ph.D. from Malaviya National Institute of Technology, Jaipur, in 2020.

He is working at LNM Institute of Information Technology, Jaipur, as an Assistant Professor from 2011 onward. He has worked as adjunct faculty at the International Institute of Information Technology (IIIT), Bangalore (Deputation at Myanmar Institute of Information Technology, Mandalay, Myanmar) for two years, and a Lecturer at Jaypee University of Engineering and Technology, Guna, for 3 semesters. His research interests are in Deep Learning, Machine learning, Natural Language Processing, cognitive modeling of language learning models, Biomedical and agricultural applications of deep learning. Sandeep is a member of IEEE since 2009 and an active member of ACM as well.

Kusum Lata has received her M.Tech. and Ph.D. degrees from Indian Institute of Technology (IIT), Roorkee, India and Indian Institute of Science (IISc), Bangalore, India in 2003 and 2010. She has also worked as a Research Associate in the Centre of Electronics Design and Technology, IISc Bangalore, India, for six months after completing her PhD. Since June 2010, She has worked as Lecturer for three years at the Indian Institute of Information Technology, Allahabad (IIIT-A) India.

She has worked as Assistant Professor from December 2013 to February 2016, and since March 2016, she is working as an Associate Professor in the Department of Electronics and Communication Engineering at The LNM Institute of Information Technology, Jaipur. She is the recipient of the Outstanding Research Paper Award in 1st Asia Symposium on Quality Electronic Design (ASQED-2009), July 15-16, 2009, Kula Lumpur, Malaysia. Her research interests include digital circuit design using FPGAs, Design for Testability, Formal Verification of Analog and Mixed Signal Designs and Hardware Security. Kusum is a member of IEEE since 2003 and an active member of ACM since 2011. She is also a lifetime member Computer Society of India.

G R Sinha is an Adjunct Professor at the International Institute of Information Technology Bangalore (IIITB) and currently deputed as Professor at Myanmar Institute of Information Technology (MIIT) Mandalay Myanmar. He obtained his B.E. (Electronics Engineering) and M.Tech. (Computer Technology) with Gold Medal from National Institute of Technology Raipur, India. He received his Ph.D. in Electronics & Telecommunication Engineering from Chhattisgarh Swami Vivekanand Technical University (CSVTU) Bhilai, India. He is Visiting Professor (Honorary) in Sri Lanka Technological Campus Colombo for one the year 2019-2020.

He has published 258 research papers, book chapters and books at the International level that includes Biometrics published by Wiley India, a subsidiary of John Wiley; Medical Image Processing published by Prentice Hall of India and 05 Edited books on Cognitive Science-Two Volumes (Elsevier), Optimization Theory (IOP) and Biometrics (Springer). He is currently editing 06 more books on Biomedical signals; Brain and behavior computing; Modern Sensors, and Data Deduplication with Elsevier, IOP, CRC Press. He is an active reviewer and editorial member of more than 12 reputed International Journals in his research areas, such as IEEE Transactions, Elsevier Journals, Springer Journals etc.

He has teaching and research experience of 21 years. He has been Dean of Faculty and Executive Council Member of CSVTU and currently a member of the Senate of MIIT. Dr Sinha has been delivering ACM lectures as ACM Distinguished Speaker in the field of DSP since 2017 across the world. His few more important assignments include Expert Member for Vocational Training Programme by Tata Institute of Social Sciences (TISS) for Two Years (2017-2019); Chhattisgarh Representative of IEEE MP Sub-Section Executive Council (2016-2019); Distinguished Speaker in the field of Digital Image Processing by Computer Society of India (2015).

He is the recipient of many awards and recognitions like TCS Award 2014 for Outstanding contributions in Campus Commune of TCS, Rajaram Bapu Patil ISTE National Award 2013 for Promising Teacher in Technical Education by ISTE New Delhi, Emerging Chhattisgarh Award 2013, Engineer of the Year Award 2011, Young Engineer Award 2008, Young Scientist Award 2005, IEI Expert Engineer Award 2007, ISCA Young Scientist Award 2006 Nomination and Deshbandhu Merit Scholarship for 05 years. He served as Distinguished IEEE Lecturer in IEEE India council for the Bombay section. He is a Senior Member of IEEE, Fellow of Institute of Engineers India and Fellow of IETE India.

He has delivered more than 50 Keynote/Invited Talks and Chaired many Technical Sessions in International Conferences across the world, such as Singapore, Myanmar, Sri Lanka, Bangalore, Mumbai, Trivandrum, Hyderabad, Mysore, Allahabad, Nagpur, Yangon, Meikhtila. His Special Session on "Deep Learning in Biometrics" was included in IEEE International Conference on Image Processing 2017. He is also a member of many National Professional bodies like ISTE, CSI, ISCA, and IEI. He is a member of the university’s various committees and has been Vice President of Computer Society of India for Bhilai Chapter for two consecutive years. He is a Consultant of various Skill Development initiatives of NSDC, Govt. of India. He is a regular Referee of Project Grants under the DST-EMR scheme and several other schemes of Govt. of India. He received a few important consultancy supports as grants and travel support.

Dr Sinha has Supervised Eight (08) PhD Scholars, 15 M. Tech. Scholars and has been Supervising 01 more PhD Scholar. His research interest includes Biometrics, Cognitive Science, Medical Image Processing, Computer Vision, Outcome based Education (OBE) and ICT tools for developing Employability Skills.

SUMMARY:
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.