A Computation-In-Memory Accelerator Based on Resistive Devices

Hoang Anh Du Nguyen, Jintao Yu, Muath Abu Lebdeh, Mottaqiallah Taouil, Said Hamdioui

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Abstract

Today's computing architectures suffer from the three well-known bottlenecks, which are the memory, the power and the instruction-level parallelism walls. Emerging non-volatile technologies, such as memristor, enable new resistive architectures that alleviate at least two of such bottlenecks, as they can process data within the memory with almost no leakage. In this paper, we propose a novel resistive computing architecture by extending a conventional architecture with a resistive based Computation-In-Memory accelerator (CIMX). We evaluate the delay, energy and area of the conventional and CIMX architecture using an analytical model and a simulation framework. The results (both based on the analytical model and simulation framework) show that the proposed architecture achieves at least one order of magnitude improvement in terms of performance, area, and energy efficiency for the considered benchmarks.
Original languageEnglish
Title of host publicationProceedings of the International Symposium on Memory Systems
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages19-32
Number of pages14
ISBN (Print)978-1-4503-7206-0
DOIs
Publication statusPublished - 2019
EventMEMSYS 2019: The International Symposium on Memory Systems - Wahington, United States
Duration: 30 Sep 20193 Oct 2019

Publication series

NameICPS: ACM International Conference Proceeding Series
PublisherACM

Conference

ConferenceMEMSYS 2019
CountryUnited States
CityWahington
Period30/09/193/10/19

Keywords

  • Computation-in-Memory
  • Accelerator
  • Resistive Computing
  • Memristor

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    Du Nguyen, H. A., Yu, J., Abu Lebdeh, M., Taouil, M., & Hamdioui, S. (2019). A Computation-In-Memory Accelerator Based on Resistive Devices. In Proceedings of the International Symposium on Memory Systems (pp. 19-32). (ICPS: ACM International Conference Proceeding Series). Association for Computing Machinery (ACM). https://doi.org/10.1145/3357526.3357554