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Title: Organismic materials for beyond von Neumann machines

Abstract

The elementary basis of intelligence in organisms with a central nervous system includes neurons and synapses and their complex interconnections forming neural circuits. In non-neural organisms such as slime mold with gel-like media, viscosity modulation enables adaptation to changing environments. At a larger scale, collective intelligence emerges via social interactions and feedback in animal colonies. Learning and memory are therefore multi-scale features that evolve as a result of constant interactions with the environment. There is growing interest in emulating such features of intelligence in computing machines and autonomous systems. Materials that can respond to their environment in a manner similar to organisms (referred to as "organismic materials") therefore may be of interest as hardware components in artificial intelligence machines. In this brief review, we present a class of semiconductors called correlated oxides as candidates for learning machines. The term "correlated" refers to the fact that electrons in such lattices strongly interact and the ground state is not what is predicted by classical band theory. Such materials can undergo insulator-metal transitions at near ambient conditions under external stimuli such as thermal or electrical fields, strain, and chemical doping. Depending on the mechanism driving the transition, intermediate states can be metastable withmore » different volatilities, and the time scales of phase change can be controlled over many orders of magnitude. The change in electronic properties can be sharp or gradual, leading to digital or analog behavior. These properties enable the realization of artificial neurons and synapses and emulate the associative and non-associative learning characteristics found in various organisms. We examine microscopic properties concerning electronic and structural transitions leading to collective behavior and theoretical treatments of the ground state and dynamical response, showcasing VO2 as a model system. Next, we briefly review algorithms designed from the plasticity demonstrated by phase changing systems. Finally, we conclude the brief review with suggestions for future research toward realizing non-von Neumann machines.« less

Authors:
ORCiD logo [1]; ORCiD logo [1];  [2];  [3]; ORCiD logo [4]; ORCiD logo [5]; ORCiD logo [6];  [4];  [3];  [7];  [1];  [1]
  1. Purdue Univ., West Lafayette, IN (United States)
  2. Univ. of Chicago, IL (United States)
  3. Univ. of California, San Diego, L Jolla, CA (United States)
  4. Pennsylvania State Univ., University Park, PA (United States)
  5. Argonne National Lab. (ANL), Lemont, IL (United States). Advanced Photon Source (APS)
  6. Argonne National Lab. (ANL), Lemont, IL (United States)
  7. Univ. of Illinois, Chicago, IL (United States); Argonne National Lab. (ANL), Lemont, IL (United States). Center for Nanoscale Materials
Publication Date:
Research Org.:
Energy Frontier Research Centers (EFRC) (United States). Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C); Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Org.:
US Air Force Office of Scientific Research (AFOSR); US Army Research Office (ARO); Gilbreth Fellowship; Vannevar Bush Faculty Fellowship; Center for Brain-Inspired Computing (CBRIC); Defense Advanced Research Projects Agency (DARPA); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division
OSTI Identifier:
1607862
Alternate Identifier(s):
OSTI ID: 1593183
Grant/Contract Number:  
AC02-06CH11357; SC0019273
Resource Type:
Accepted Manuscript
Journal Name:
Applied Physics Reviews
Additional Journal Information:
Journal Volume: 7; Journal Issue: 1; Journal ID: ISSN 1931-9401
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING

Citation Formats

Zhang, Hai-Tian, Panda, Priyadarshini, Lin, Jerome, Kalcheim, Yoav, Wang, Kai, Freeland, John W., Fong, Dillon D., Priya, Shashank, Schuller, Ivan K., Sankaranarayanan, Subramanian K. R. S., Roy, Kaushik, and Ramanathan, Shriram. Organismic materials for beyond von Neumann machines. United States: N. p., 2020. Web. doi:10.1063/1.5113574.
Zhang, Hai-Tian, Panda, Priyadarshini, Lin, Jerome, Kalcheim, Yoav, Wang, Kai, Freeland, John W., Fong, Dillon D., Priya, Shashank, Schuller, Ivan K., Sankaranarayanan, Subramanian K. R. S., Roy, Kaushik, & Ramanathan, Shriram. Organismic materials for beyond von Neumann machines. United States. https://doi.org/10.1063/1.5113574
Zhang, Hai-Tian, Panda, Priyadarshini, Lin, Jerome, Kalcheim, Yoav, Wang, Kai, Freeland, John W., Fong, Dillon D., Priya, Shashank, Schuller, Ivan K., Sankaranarayanan, Subramanian K. R. S., Roy, Kaushik, and Ramanathan, Shriram. Wed . "Organismic materials for beyond von Neumann machines". United States. https://doi.org/10.1063/1.5113574. https://www.osti.gov/servlets/purl/1607862.
@article{osti_1607862,
title = {Organismic materials for beyond von Neumann machines},
author = {Zhang, Hai-Tian and Panda, Priyadarshini and Lin, Jerome and Kalcheim, Yoav and Wang, Kai and Freeland, John W. and Fong, Dillon D. and Priya, Shashank and Schuller, Ivan K. and Sankaranarayanan, Subramanian K. R. S. and Roy, Kaushik and Ramanathan, Shriram},
abstractNote = {The elementary basis of intelligence in organisms with a central nervous system includes neurons and synapses and their complex interconnections forming neural circuits. In non-neural organisms such as slime mold with gel-like media, viscosity modulation enables adaptation to changing environments. At a larger scale, collective intelligence emerges via social interactions and feedback in animal colonies. Learning and memory are therefore multi-scale features that evolve as a result of constant interactions with the environment. There is growing interest in emulating such features of intelligence in computing machines and autonomous systems. Materials that can respond to their environment in a manner similar to organisms (referred to as "organismic materials") therefore may be of interest as hardware components in artificial intelligence machines. In this brief review, we present a class of semiconductors called correlated oxides as candidates for learning machines. The term "correlated" refers to the fact that electrons in such lattices strongly interact and the ground state is not what is predicted by classical band theory. Such materials can undergo insulator-metal transitions at near ambient conditions under external stimuli such as thermal or electrical fields, strain, and chemical doping. Depending on the mechanism driving the transition, intermediate states can be metastable with different volatilities, and the time scales of phase change can be controlled over many orders of magnitude. The change in electronic properties can be sharp or gradual, leading to digital or analog behavior. These properties enable the realization of artificial neurons and synapses and emulate the associative and non-associative learning characteristics found in various organisms. We examine microscopic properties concerning electronic and structural transitions leading to collective behavior and theoretical treatments of the ground state and dynamical response, showcasing VO2 as a model system. Next, we briefly review algorithms designed from the plasticity demonstrated by phase changing systems. Finally, we conclude the brief review with suggestions for future research toward realizing non-von Neumann machines.},
doi = {10.1063/1.5113574},
journal = {Applied Physics Reviews},
number = 1,
volume = 7,
place = {United States},
year = {Wed Jan 22 00:00:00 EST 2020},
month = {Wed Jan 22 00:00:00 EST 2020}
}

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