VLSI-ML2021: VLSI and Hardware Implementations using Modern Machine Learning Methods |
Website | https://vesd.lnmiit.ac.in/VLSI-ML/ |
Submission link | https://easychair.org/conferences/?conf=vlsiml2021 |
Poster | download |
Submission deadline | November 22, 2020 |
Abstract Notification | November 28, 2020 |
Full Chapter Submission | December 15, 2020 |
Revision Notification | December 31, 2020 |
Final Submission | January 25, 2021 |
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 editors on sandeep.saini@lnmiit.ac.in, kusum@lnmiit.ac.in, and gr_sinha@miit.edu.mm for any clarification. The submission template is available at http://vesd.lnmiit.ac.in/VLSI-ML/ or can be directly downloaded from here.
Submissions can be made via easychair.
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
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Sandeep Saini sandeep.saini@lnmiit.ac.in
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Dr. Kusum Lata kusum@lnmiit.ac.in
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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