2020 MRS Spring Meeting

Call for Papers

Symposium EL08—Neuromorphic Materials and Devices for Bio-inspired Computing and Artificial Intelligence

With the advent of effective artificial neural network implementations, impressive progress has been made in recent years in realizing human-level cognitive capabilities. However, solving cognitive problems on today’s digital computers is already considered as a challenging task which requires extended computation time and datacenter-scale computational resources. Therefore, there has been increasing interest in the field of neuromorphic computing to tackle such problems on native hardware and bio-inspired architecture, with an expectation of achieving a brain-like efficiency and large performance gain as well as artificial intelligence. This requires a systematic approach combining the expertise of material scientists to understand how learning happens in artificial neural networks and biological systems, and identify the key components which can be cast into material and device solutions. The goal of this symposium is to provide a forum to unite researchers who are engaged in the study of material research across neuromorphic computing technologies. This symposium will cover the scientific and technological exploration and advances of the development, characterization and system-level integration of new materials and devices for a variety of neuromorphic applications. Invited talks will attempt to bridge the gap between interdisciplinary topics such as material science, device engineering, computer science, algorithms, systems and architecture-level considerations in order to accelerate the discussion and development of neuromorphic materials toward practical applications.

Topics will include:

  • New materials and devices for implementing artificial neural networks
  • Electronic synapse design and characterization
  • Deep learning and its implication on materials research
  • Material/device specification for neuromorphic applications
  • Material and device characterization for bio-inspired computing
  • Realization of neuron functions by novel materials and devices
  • Memristive switching and physics
  • Material characterization methodology for neuromorphic applications
  • Machine learning algorithms implementable by neuromorphic devices
  • New materials and architectures for implementing machine learning algorithms in native hardware
  • Multi-terminal device and its material investigation for brain-inspired computing
  • Neuromorphic sensors and its materials
  • Connectivity and operation of electrical synaptic networks and systems
  • Resistive switching-based synaptic devices
  • Brain-inspired computing system and its applications
  • Neuromorphic system and algorithm
  • Devices for neuromorphic computation
  • New concept and novel materials for neuromorphic devices
  • A tutorial complementing this symposium is tentatively planned.

Invited Speakers:

  • Elliot Fuller (Sandia National Laboratories, USA)
  • Tayfun Gokmen (IBM T.J. Watson Research Center, USA)
  • Tuo-Hung Hou (National Chiao Tung University, Taiwan)
  • Jang-Sik Lee (Pohang University of Science and Technology, Republic of Korea)
  • Wei Lu (University of Michigan–Ann Arbor, USA)
  • Dmitry Strukov (University of California, Santa Barbara, USA)
  • Joshua Yang (University of Massachusetts Amherst, USA)
  • Yuchao Yang (Peking University, China)
  • Bilge Yildiz (Massachusetts Institute of Technology, USA)
  • Shimeng Yu (Georgia Institute of Technology, USA)

Symposium Organizers

Seyoung Kim
IBM TJ Watson Research Center
USA
9149451323, sykim@us.ibm.com

Ru Huang
Peking University
Institute of Microelectronics
China
+86 10-62757761, ruhuang@pku.edu.cn

Jeehwan Kim
Massachusetts Institute of Technology
Department of Mechanical Engineering
USA
617.324.1948, jeehwan@mit.edu

Ilia Valov
Forschungszentrüm Juelich GmbH
Electronic Materials (PGI-7)
Germany
+49 2461 61 2994, i.valov@fz-juelich.de