2018 MRS Spring Meeting

Call for Papers

Late Breaking Abstract Submission Closed
January 11, 2018 (11:59 pm ET)

Symposium EP06—Materials, Devices and Systems for Machine Learning and Neuromorphic Computing

Machine learning algorithms are revolutionizing many aspects of human endeavor such as business analytics, social media interactions, internet of things (IoT), autonomous navigation and healthcare. However, the computing platforms that are employed today to analyze, learn and infer insights from the big data streams associated with most of these applications are not suited for efficient real-time analysis and consumes significant amounts of power and computational effort. This has motivated the exploration of new devices based on novel materials that will enable area and power efficient computational systems that are more naturally suited for these applications. The goal of this symposium is to provide an overview of current active topics of research in materials science targeted at developing new nanoscale devices for machine learning and neuromorphic computing. The symposium will feature invited abstracts from researchers that will present high-level perspectives on machine learning trends and system-level requirements as well as cutting-edge developments on materials for these applications based on chalcogenides, metal-oxides, magnetic materials, ferroelectric materials etc.

Topics will include:

  • Machine learning trends and requirements from new materials and technologies
  • Materials and devices for the acceleration of deep learning
  • Materials and devices for brain-inspired spiking neural networks
  • Materials and devices for approximate and stochastic computing
  • Memristive synaptic devices for brain inspired computing including short term and long term plasticity
  • Nanoscale device phenomena for neuro-inspired/machine learning-inspired computing platforms
  • Theoretical investigation and modeling of novel materials for brain-inspired computing systems
  • Target applications for neuromorphic systems based on novel materials and devices
  • A tutorial complementing this symposium is tentatively planned.

Invited Speakers:

  • Fabien Alibart (CNRS, France)
  • Geoff Burr (IBM Research - Almaden, USA)
  • Shubhra Gangopadhyay (National Science Foundation, USA)
  • Udayan Ganguly (Indian Institute of Technology Bombay, India)
  • Julie Grollier (CNRS, France)
  • Hyunsang Hwang (Pohang University of Science and Technology, Republic of Korea)
  • Subramanian Iyer (University of California, Los Angeles, USA)
  • Yusuf Leblebici (École Polytechnique Fédérale de Lausanne, Switzerland)
  • Robert Legenstein (TU Graz, Austria)
  • Blanka Magyari-Kope (Stanford University, USA)
  • Shubhasish Mitra (Stanford University, USA)
  • Themis Prodromakis (University of Southampton, United Kingdom)
  • Sabina Spiga (CNR-IMM, Italy)
  • John Paul Strachan (HP Labs, USA)
  • Angel Yanguas-Gil (Argonne National Laboratory, USA)

Symposium Organizers

Bipin Rajendran
New Jersey Institute of Technology
Electrical and Computer Engineering
973-596-3516, bipin@njit.edu

Duygu Kuzum
University of California, San Diego
Electrical and Computer Engineering
858-534-2985, dkuzum@eng.ucsd.edu

Abu Sebastian
IBM Research - Zurich
41-43-539-7572, ZRLASE@ch.ibm.com

Manan Suri
Indian Institute of Technology Delhi
Electrical Engineering
91-11-2659-1146, manansuri@ee.iitd.ac.in

Keywords for Abstract Submission

Deep learning acceleration, Electronic neuron and synapse, Memristors, Nanoscale devices, Neuromorphic computing, Spiking neural networks