Nanoscale electronic synapses using phase change devices

Bryan L. Jackson, Bipin Rajendran, Gregory S. Corrado, Matthew Breitwisch, Geoffrey W. Burr, Roger Cheek, Kailash Gopalakrishnan, Simone Raoux, Charles T. Rettner, Alvaro Padilla, Alex G. Schrott, Rohit S. Shenoy, Bülent N. Kurdi, Chung H. Lam, Dharmendra S. Modha

Research output: Contribution to journalArticlepeer-review

110 Scopus citations

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 (∼1011 synapses per sq cm) and energy efficiency (consuming ∼1pJ per synaptic programming event) of the human brain.

Original languageEnglish (US)
Article number12
JournalACM Journal on Emerging Technologies in Computing Systems
Volume9
Issue number2
DOIs
StatePublished - Oct 21 2013
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Keywords

  • Chalcogenide
  • Phase change memory
  • Spike timing dependent plasticity

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