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This content will become publicly available on November 23, 2018

Title: Memristive Mixed-Signal Neuromorphic Systems: Energy-Efficient Learning at the Circuit-Level

Neuromorphic computing is non-von Neumann computer architecture for the post Moore’s law era of computing. Since a main focus of the post Moore’s law era is energy-efficient computing with fewer resources and less area, neuromorphic computing contributes effectively in this research. Here in this paper, we present a memristive neuromorphic system for improved power and area efficiency. Our particular mixed-signal approach implements neural networks with spiking events in a synchronous way. Moreover, the use of nano-scale memristive devices saves both area and power in the system. We also provide device-level considerations that make the system more energy-efficient. The proposed system additionally includes synchronous digital long term plasticity, an online learning methodology that helps the system train the neural networks during the operation phase and improves the efficiency in learning considering the power consumption and area overhead.
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  1. Univ. of Tennessee, Knoxville, TN (United States). Dept. of Electrical Engineering and Computer Science
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Grant/Contract Number:
AC05-00OR22725; FA8750-16-1-0065
Accepted Manuscript
Journal Name:
IEEE Journal on Emerging and Selected Topics in Circuits and Systems
Additional Journal Information:
Journal Volume: 8; Journal Issue: 1; Journal ID: ISSN 2156-3357
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Country of Publication:
United States
77 NANOSCIENCE AND NANOTECHNOLOGY; 97 MATHEMATICS AND COMPUTING; Memristor; simulator; emerging technology; neuromorphic computing
OSTI Identifier: