Rapid in-memory matrix multiplication using Associative Processor

Mohamed Ayoub Neggaz, Hasan Erdem Yantir, Smail Niar, Ahmed Eltawil, Fadi Kurdahi

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

6 Scopus citations


Memory hierarchy latency is one of the main problems that prevents processors from achieving high performance. To eliminate the need of loading/storing large sets of data, Resistive Associative Processors (ReAP) have been proposed as a solution to the von Neumann bottleneck. In ReAPs, logic and memory structures are combined together to allow inmemory computations. In this paper, we propose a new algorithm to compute the matrix multiplication inside the memory that exploits the benefits of ReAP. The proposed approach is based on the Cannon algorithm and uses a series of rotations without duplicating the data. It runs in O(n), where n is the dimension of the matrix. The method also applies to a large set of row by column matrix-based applications. Experimental results show several orders of magnitude increase in performance and reduction in energy and area when compared to the latest FPGA and CPU implementations.
Original languageEnglish (US)
Title of host publicationProceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9783981926316
StatePublished - Apr 19 2018
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2019-11-20


Dive into the research topics of 'Rapid in-memory matrix multiplication using Associative Processor'. Together they form a unique fingerprint.

Cite this