Neuromorphic computing—meaning microprocessors configured more like human brains than like traditional computer chips—is one of the most promising transformative computing technologies currently under study.
Such neuromorphic computing in which microprocessors are configured more like human brains is one of the most promising transformative computing technologies currently under study.
J. Joshua Yang and Qiangfei Xia are professors in the electrical and computer engineering department in the UMass Amherst College of Engineering. Yang describes the research as part of collaborative work on a new type of memristive device.
Memristive devices are electrical resistance switches that can alter their resistance based on the history of applied voltage and current. These devices can store and process information and offer several key performance characteristics that exceed conventional integrated circuit technology.
“Memristors have become a leading candidate to enable neuromorphic computing by reproducing the functions in biological synapses and neurons in a neural network system, while providing advantages in energy and size,” the researchers say.
Neuromorphic computing—meaning microprocessors configured more like human brains than like traditional computer chips—is one of the most promising transformative computing technologies currently under intensive study. Xia says, “This work opens a new avenue of neuromorphic computing hardware based on memristors.”
They say that most previous work in this field with memristors has not implemented diffusive dynamics without using large standard technology found in integrated circuits commonly used in microprocessors, microcontrollers, static random access memory and other digital logic circuits.
The researchers say they proposed and demonstrated a bio-inspired solution to the diffusive dynamics that is fundamentally different from the standard technology for integrated circuits while sharing great similarities with synapses. They say, “Specifically, we developed a diffusive-type memristor where diffusion of atoms offers a similar dynamics and the needed time-scales as its bio-counterpart, leading to a more faithful emulation of actual synapses, i.e., a true synaptic emulator.”
The researchers say, “The results here provide an encouraging pathway toward synaptic emulation using diffusive memristors for neuromorphic computing.”
The title of the article is “Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing.” In addition to Xia and Yang, the authors include Zhongrui Wang, Saumil Joshi, Hao Jiang, Rivu Midya, Peng Lin, of the UMass Amherst electrical and computer engineering department; Sergey E. Savel’ev of the department of physics, Loughborough University in the U.K.; Miao Hu, Ning Ge, John Paul Strachan, Zhiyong Li, and R. Stanley Williams of the Hewlett Packard Labs, Palo Alto, Calif.; Qing Wu and Mark Barnell of the Air Force Research Lab, Information Directorate, Rome, New York; GengLin Li of the UMass Amherst department of biology, and Huolin L. Xin of the Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York.
UMass Amherst News Office
Image Caption: In situ TEM observation of the threshold switching process suggesting the relaxation is a diffusion process driven by interfacial energy minimization.
External electric field is exerted at the point of time zero. Ag migration is observed at time 0.1 s when two nanocrystals (orange and blue arrows) started to form. A clear arc-shaped filament is visible at 2.5 s (red arrow). When the external biasing is removed at 5.0 s, the filament starts to deform, shrinking to a round spherical nanocluster implying an interfacial energy driven diffusion mechanism. All scale bars, 20 nm.