Efficiency and Performance for Connectivity Constrained Computing

Victor Zhirnov, Director & Chief Scientist

This research is aimed at establishing a revolutionary computing paradigm as the new fundamental platform
for all future applications from mobile to data centers.


This program facilitates collaborative research 'from materials to architecture' aiming at establishing revolutionary and sustainable computing paradigms. The ultimate goal is to create revolutionary highly-efficient computing platforms to enable neuro-inspired cognitive and learning abilities which will be required to address the vast range of future data types and workloads. A hybrid platform is envisioned that will expand current programmable computational methodologies to include neuro-inspired cognitive and learning abilities, with dramatic improvements in energy efficiency.

Research Focus

This is a cross-disciplinary, collaborative research effort aimed at establishing a revolutionary and sustainable computing paradigm. The primary areas of research are:

  1. Revolutionary computing platforms co-optimized from architecture to devices to enable orders of magnitude improvement in energy efficiency;
  2. New processor architectures to support advanced deep learning algorithms;
  3. Neuro-inspired solutions for machine cognition.

E2CDA Projects Launched

  1. On October 1, 2016, four new collaborative research projects launched—selected from the E2CDA joint solicitation with NSF and SRC’s NRI program, and managed within the EP3C thrust.

EP3C Metrics

  1. Current

    10 Research Tasks
    10 Universities
    21 Students
    19 Faculty Researchers
    21 Liaison Personnel
  2. This Year

    6 Research Publications
  3. Last Year

    53 Research Publications
  4. Since Inception

    26 Research Tasks
    22 Universities
    64 Students
    41 Faculty Researchers
    70 Liaison Personnel
    284 Research Publications
    4 Patent Applications
Updated: 19-Mar-2019, 12:05 a.m. ET

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