Call for Chapters: Neuromorphic Computing Systems for Industry 4.0

Editors

Dr. S. Dhanasekar, Department of ECE, Sri Eshwar College of Engineering, Coimbatore, India
Dr. K. Martin Sagayam, Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India
Ms. Surbhi Vijh, Department of Information Technology, KIET Group of Institutions, India
Prof. (Dr.) Vipin Tyagi, Jaypee University of Engineering & Technology, Guna, Madhya Pradesh, India
Prof. (Dr.) Alex Norta, Tallinn University of Technology, Tallinn, Estonia.

Call for Chapters

Proposals Submission Deadline: July 25, 2022
Full Chapters Due: October 7, 2022

Introduction

As artificial intelligence (AI) processing moves from the cloud to the edge of the network, battery powered and deeply embedded devices are challenged to perform AI functions—like computer vision and voice recognition. Microchip Technology Inc. (Nasdaq: MCHP), via its Silicon Storage Technology (SST) subsidiary, is addressing this challenge by significantly reducing power with its analog memory technology, the memBrain neuromorphic memory solution. Based on its industry proven SuperFlash technology and optimized to perform vector matrix multiplication (VMM) for neural networks, Microchip’s analog flash memory solution improves system architecture implementation of VMM through an analog in-memory compute approach, enhancing AI inference at the edge. The memBrain solution is being adopted by today’s companies looking to advance machine learning capacities in edge devices. Due to its ability to significantly reduce power, this analog in-memory compute solution is ideal for any AI application.

Objective

The comprehensive and timely publication aims to be an essential reference source, on the available literature in the field of Neural computing-based Microchip Technology. This will provide further research opportunities in these dynamic fields. It is hoped that this handbook will provide the resources necessary for researchers, advanced level students, technology developers to adopt and implement the advances in technology and applications.

Target Audience

Academicians, researchers, advanced-level students, and technology developers will find this text useful in furthering their research exposure to pertinent topics advanced technology and applications in Neural computing based Microchip technology for Industry 4.0. This will provide assistance in furthering and strengthen their own research efforts in these fields.

Recommended Topics

- Mixed-analog-digital multichip system for large-scale neural simulations - Neuromorphic electronic circuits for building autonomous cognitive systems - A Neuromorphic many core processor with on-chip learning. - Neuromorphic controller implemented on VLSI devices - Neuromorphic Control for Autonomous Robotic Navigation - VLSI architectures for spiking deep neural networks - VLSI Implementation of Neural Systems - Development of a Neuromorphic Computing System - Spiking deep convolutional neural networks for energy-efficient object recognition - A spiking-neuron integrated circuit with communication network and interface - Development of a neuromorphic computing system - A neuromorphic hardware co-processor based on spiking neural networks - A chip based Robotic manipulators using neural network architectures - Neural network architectures for robotic applications - Evolving spiking neural networks for robot control - Neural network based network on chip architecture - Digital implementation of a spiking neural network (SNN) - VLSI Spiking Neural System for the Development of Autonomous Agents - Biologically inspired SNN for robot control - Neuromorphic implementations of neurobiological learning algorithms. - Energy and performance analysis of Robotic applications using artificial neural network.

Submission Procedure

Researchers and practitioners are invited to submit on or before July 25, 2022, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by August 8, 2022 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by October 7, 2022, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Neuromorphic Computing Systems for Industry 4.0. All manuscripts are accepted based on a double-blind peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.



Publisher

This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2023.



Important Dates

July 25, 2022: Proposal Submission Deadline
August 8, 2022: Notification of Acceptance
October 7, 2022: Full Chapter Submission
November 20, 2022: Review Results Returned
December 1, 2022: Final Acceptance Notification
December 15, 2022: Final Chapter Submission



Inquiries

Dr. S. Dhanasekar
Department of ECE, Sri Eshwar College of Engineering, Coimbatore, India
dhanasekar.sm@gmail.com

Dr. K. Martin Sagayam
Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India
martinsagayam.k@gmail.com

Ms. Surbhi Vijh
Department of Information Technology, KIET Group of Institutions, India
surbhivijh428@gmail.com

Prof. (Dr.) Vipin Tyagi
Jaypee University of Engineering & Technology, Guna, Madhya Pradesh, India
dr.vipin.tyagi@gmail.com

Prof. (Dr.) Alex Norta
Tallinn University of Technology, Tallinn, Estonia.
alex.norta.phd@ieee.org


Dr. Mayank Singh (Managing Editor)
Department of Information Technology, KIET Group of Institutions, India
dr.mayank.singh@ieee.org



Classifications


Computer Science and Information Technology
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