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Author Jackson, Bryan L. ♦ Rajendran, Bipin ♦ Corrado, Gregory S. ♦ Breitwisch, Matthew ♦ Burr, Geoffrey W. ♦ Cheek, Roger ♦ Gopalakrishnan, Kailash ♦ Raoux, Simone ♦ Rettner, Charles T. ♦ Padilla, Alvaro ♦ Schrott, Alex G. ♦ Shenoy, Rohit S. ♦ Kurdi, Blent N. ♦ Lam, Chung H. ♦ Modha, Dharmendra S.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2013
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Chalcogenide ♦ Phase change memory ♦ Spike timing dependent plasticity
Abstract The memory capacity, computational power, communication bandwidth, energy consumption, and physical size of the brain all tend to scale with the number of synapses, which outnumber neurons by a factor of 10,000. Although progress in cortical simulations using modern digital computers has been rapid, the essential disparity between the classical von Neumann computer architecture and the computational fabric of the nervous system makes large-scale simulations expensive, power hungry, and time consuming. Over the last three decades, CMOS-based neuromorphic implementations of “electronic cortex” have emerged as an energy efficient alternative for modeling neuronal behavior. However, the key ingredient for electronic implementation of any self-learning system—programmable, plastic Hebbian synapses scalable to biological densities—has remained elusive. We demonstrate the viability of implementing such electronic synapses using nanoscale phase change devices. We introduce novel programming schemes for modulation of device conductance to closely mimic the phenomenon of Spike Timing Dependent Plasticity (STDP) observed biologically, and verify through simulations that such plastic phase change devices should support simple correlative learning in networks of spiking neurons. Our devices, when arranged in a crossbar array architecture, could enable the development of synaptronic systems that approach the density $(∼10^{11}$ synapses per sq cm) and energy efficiency (consuming ∼1pJ per synaptic programming event) of the human brain.
ISSN 15504832
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2013-05-01
Publisher Place New York
e-ISSN 15504840
Journal ACM Journal on Emerging Technologies in Computing Systems (JETC)
Volume Number 9
Issue Number 2
Page Count 20
Starting Page 1
Ending Page 20


Source: ACM Digital Library