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VLSI-ML2021: VLSI and Hardware Implementations using Modern Machine Learning Methods

Jaipur
Organization: The LNMIIT Jaipur
Categories: Engineering
Event Date: 2021-01-25 to 2021-01-25 Abstract Due: 2020-11-10

About The Book
IC design and fabrication has been following Moore’s law and being developed as a faster and smaller product in every next generation. With the limitation on the technology size, the progress is stalled and alternate methods are being explored to keep up the progress in this field. Machine learning based solutions are appearing as a good alternative for resolving these issues. Today machine learning based models and architectures are being employed in VLSI design, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. The book contains chapters on case studies as well as novel research ideas in the given field.

In a nutshell, the book will cover the following topics of research.

Details of state-of-the-art Machine Learning methods used in VLSI Design.
Description of the Hardware implementation of machine learning algorithms.
Machine learning methods for VLSI architectures implementation.
Machine learning approached for reconfigurable computing
Device modeling using Machine learning algorithms
Submission Guidelines
All Chapters must be original and not simultaneously submitted to another journal or conference. You can contact the editor on sandeep.saini@lnmiit.ac.in for any clarification. 

List of Topics
Modern Machine learning methods for VLSI applications
VLSI Implementation of Deep Neural Network
Spike-driven synaptic plasticity theory, simulation and VLSI implementation
Machine Learning methods for Hardware Security
Machine Learning approaches for FPGA implementation
Image Processing with FPGA implementation
Hardware based Sign Language Recognition
Machine learning implementation on FPGA using partial reconfiguration
Hardware based framework for accelerating statistical machine learning
SRAM computation-in-memory macro for multiple-bit CNN-based machine learning
Machine Learning methods for IC testing
Machine learning approaches for yield management in semiconductor manufacturing
Machine learning systems for intelligent services in the IoT
Machine learning methods for hardware performance optimization
System on Chip design using machine learning
Machine learning for chip fabrication
Future directions for machine learning based hardware systems
Other relevant topics


Editors 
Book Editors
Sandeep Saini    sandeep.saini@lnmiit.ac.in
Dr. Kusum Lata    kusum@lnmiit.ac.in
Prof. G R Sinha    gr_sinha@miit.edu.mm
 
Publication
VLSI-ML2021 book will be published by CRC press of Taylor and Francis group. The published book will also be submitted for SCOPUS indexing. 

 

Contact
All questions about submissions should be emailed to . sandeep.saini@lnmiit.ac.in, kusum@lnmiit.ac.in and gr_sinha@miit.edu.mm

https://vesd.lnmiit.ac.in/VLSI-ML/

sandeep.saini@lnmiit.ac.in

Sandeep Saini