Symposium EM07—Materials, Devices and Architectures for Neuromorphic Engineering and Brain-Inspired Computing

Advances in artificial neural networks and Big Data analytics have begun to deliver impressive learning, reasoning and human-machine interaction capabilities on computing hardware founded on silicon CMOS technology. However, the current approach to solve such cognitive problems on conventional computers is universally considered as a time-consuming task which requires datacenter-scale computational resources. Therefore, there has been increasing interest in the field of neuromorphic engineering and brain-inspired computing to build such networks on native hardware, with an expectation of achieving a brain-like efficiency and large performance gain as well as new functionalities. This requires a systematic approach combining the expertise of material scientists to study, refine and develop neuromorphic materials with architecture-level understanding.

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. The materials of interest include: resistive switching materials, conductive filament materials, phase-change, ferroelectric and tunneling-based materials. Theoretical and modeling studies to define clear performance metrics, or investigating material and device requirements for specific algorithms and architectures, are also welcome. Invited talks will attempt to bridge the gap between interdisciplinary topics such as material science/engineering, neuroscience, computer science, algorithms, systems and architecture-level considerations in order to accelerate the discussion and development of these materials toward practical applications.

Topics will include:

  • Material characterization for neuromorphic applications
  • Electronic synapse design and characterization
  • Memristors and resistive switching materials and devices
  • Conductive filament materials and devices
  • Neuromorphic devices, sensors and materials and their applications
  • Theory of electrically configurable materials including but not limited to phase change materials, resistive change materials, oxides, magnetic, carbon-based materials and polymers
  • New materials and devices for implementing artificial neural networks
  • Multi-terminal device and its material investigation for brain-inspired computing
  • New materials and architectures for implementing machine learning algorithms in native hardware
  • Connectivity and operation of electrical synaptic networks and systems
  • Theory/modeling of material and device requirements for neuromorphic applications
  • A tutorial complementing this symposium is tentatively planned.

Invited Speakers:

  • Yoshua Bengio (University of Montreal, Canada)
  • Tayfun Gokmen (IBM T.J. Watson Research Center, USA)
  • Julie Grollier (Centre National de la Recherche Scientifique, France)
  • Tuo-Hung Hou (National Chiao-Tung University, Taiwan)
  • Daniele Ielmini (Polytech Milan, Italy)
  • Doo Seok Jeong (Korea Institute of Science and Technology, Republic of Korea)
  • Rashimi Jha (University of Cincinnati, USA)
  • Matthew Marinella (Sandia National Laboratories, USA)
  • Shriram Ramanathan (Purdue University, USA)
  • Jennifer Rupp (Massachusetts Institute of Technology, USA)
  • Manan Suri (Indian Institute of Technology, Delhi, India)
  • Naveen Verma (Princeton University, USA)
  • Elisa Vianello (CEA-LETI, France)
  • Rainer Waser (RWTH-Aachen and FZ Juelich, Germany)
  • Joshua Yang (University of Massachusetts Amherst, USA)
  • Yuchao Yang (Peking University, China)

Symposium Organizers

Seyoung Kim
IBM T.J. Watson Research Center
USA
914-945-1323, sykim@us.ibm.com

Barbara De Salvo
Leti, CEA-TECH
France
33-4-38-78-64-97, barbara.desalvo@cea.fr

Hyunsang Hwang
Pohang University of Science and Technology
Department of Materials Science and Engineering
Republic of Korea
82-54-279-2155, hwanghs@postech.ac.kr

Shimeng Yu
Arizona State University
Electrical Engineering and Computer Engineering
USA
480-727-1900, shimeng.yu@asu.edu