Neuromorphic Technologies for Novel Hardware AI – Now Open for Submissions!

Submit to this Special Topic today View in Browser SUBMIT AN ARTICLE MANAGE MY ALERTS ABOUT THE JOURNAL APL Machine Learning Call for Papers Special Topic: Neuromorphic Technologies for Novel Hardware AI The rise of artificial intelligence requires a growing amount of computing power that is not sustainable with current computing technologies. In this context, the field of neuromorphic and in-memory computing has made great advancements in recent times to fabricate hardware that can perform machine learning tasks at a substantially lower energy and on-the-edge, minimizing the latency issues with cloud computing, making them all the more suitable for mission critical, surveillance and remote applications where on-the-fly decision making is key. This Special Topic invites submissions focusing on various energy efficient approaches to realize hardware AI, through novel materials and devices, fabrication strategies, computing architectures, neuromorphic systems, and brain-inspired hardware-aware algorithms. Submissions are encouraged towards hardware AI applied to specific practical use cases in different technology domains, other than MNIST data sets, and also hardware AI for solving specific complex mathematical problems such as optimization, differential equation solvers etc. Hardware AI through (but not limited to): • Novel Materials, Devices and Systems for Neuromorphic Computing • In materia computing • Hardware Architectures for Brain-inspired Computing • Modeling Neuromorphic Devices and Architectures • Hardware-Aware Neuromorphic Algorithms • Neuromorphic Computing for Domain Specific End Use Applications • Neuromorphic Computing applications (for solving complex problems such as optimization, Partial Differential Equations (PDEs), and more) • Design and Technology Co-Optimization (DTCO) for neuromorphic architectures • Hardware-software codesign Guest Editors Gianluca Milano, Researcher Italian Institute of Metrological Research (INRM) Giacomo Pedretti, Senior Research Scientist Hewlett Packard Labs, California, USA Nagarajan Raghavan, Associate Professor Engineering Product Development (EPD) Pillar Singapore University of Technology and Design (SUTD), Singapore Submission Deadline: June 30, 2024 LEARN MORE > SNEAK PEEK AT NEXT ISSUE M3ICRO: Machine learning-enabled compact photonic tensor core based on programmable multi-operand multimode interference Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Zixuan Jiang Noise tailoring, noise annealing, and external perturbation injection strategies in memristive Hopfield neural networks János Gergő Fehérvári, Zoltán Balogh, Tímea Nóra Török, András Halbritter Bring memristive in-memory computing into general-purpose machine learning: A perspective Houji Zhou, Jia Chen; Jiancong Li, Ling Yang Tell Us What You Think! Did you like this email? Yes > No >  Follow us on social media Copyright © 2024 AIP Publishing. All rights reserved. 1305 Walt Whitman Rd., Melville, NY 11747 You are receiving this email because you have opted-in to receive alerts from us. To guarantee delivery of this email please add journals@aip-info.org to your address book and safe senders list. If you no longer wish to receive emails from us then please unsubscribe or amend your settings.