In-Memory Execution Of Compute Kernels Using Flow-Based Memristive Crossbar Computing

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.

Publication Date

11-28-2017

Publication Title

2017 IEEE International Conference on Rebooting Computing, ICRC 2017 - Proceedings

Volume

2017-January

Number of Pages

1-6

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICRC.2017.8123643

Socpus ID

85043485668 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85043485668

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