In-memory execution of compute kernels using flow-based memristive crossbar computing

Dwaipayan Chakraborty, Sunny Raj, Julio Cesar, Gutierrez Troyle, Thomas Sumit, Kumar Jha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Rebooting computing using in-memory architectures relies on the ability of emerging devices to execute a legacy software stack. In this paper, we present our approach of executing compute kernels written in a subset of the C programming language using flow-based computing on nanoscale memristor crossbars. Our approach also tests the correctness of the design using the parallel Xyces electronic simulation software. We demonstrate the potential of our approach by designing and testing a compute kernel for edge detection in images.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Rebooting Computing, ICRC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538615539
DOIs
StatePublished - Nov 28 2017
Externally publishedYes
Event2017 IEEE International Conference on Rebooting Computing, ICRC 2017 - Washington, United States
Duration: Nov 8 2017Nov 9 2017

Publication series

Name2017 IEEE International Conference on Rebooting Computing, ICRC 2017 - Proceedings
Volume2017-January

Conference

Conference2017 IEEE International Conference on Rebooting Computing, ICRC 2017
Country/TerritoryUnited States
CityWashington
Period11/8/1711/9/17

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Statistical and Nonlinear Physics
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Hardware and Architecture
  • Software

Fingerprint

Dive into the research topics of 'In-memory execution of compute kernels using flow-based memristive crossbar computing'. Together they form a unique fingerprint.

Cite this