Modeling and benchmarking computing-in-memory for design space exploration

Dayane Reis, Di Gao, Shaahin Angizi, Xunzhao Yin, Deliang Fan, Michael Niemier, Cheng Zhuo, X. Sharon Hu

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

9 Scopus citations


The bottleneck between the limited memory bandwidth and high speed processing demands is the main cause of problems associated with high volume of data transfers in data-intensive applications. As a possible remedy to these issues, computing-in-memory (CiM) enables a subset of logic and arithmetic operations to be performed where the data resides, i.e., inside the memory. Various CiM designs have been proposed to date, based on different technologies. Given the variety of options available, picking the right design option for a system/application can be a complex task. When choosing a CiM design, it is important to establish evaluation conditions that are as uniform as possible to make a fair choice between available design options. In this paper, we describe a methodology for an uniform benchmarking of CiM designs. Our approach evaluates devices/circuits, arrays and the overall impact of CiM to a system with a framework based on Eva-CiM. As a case study, we analyze the array-level performance of 7 recent CiM designs implemented with SRAM, DRAM, FeFET-RAM, STT-MRAM, SOT-MRAM, and RRAM. After we identify that the FeFET-RAM-based design shows promising energy and delay savings at the array level, we carry out a system level evaluation showing that FeFET-RAM-based CiM outperforms a CMOS SRAM CiM baseline by an average of 60% across a set of 17 benchmarks (with respect to energy savings). Regarding speedups, both technologies offer virtually the same benefit of about ∼1.5× when compared to a situation where processing does not happen in memory.

Original languageEnglish (US)
Title of host publicationGLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI
PublisherAssociation for Computing Machinery
Number of pages6
ISBN (Electronic)9781450379441
StatePublished - Sep 7 2020
Externally publishedYes
Event30th Great Lakes Symposium on VLSI, GLSVLSI 2020 - Virtual, Online, China
Duration: Sep 7 2020Sep 9 2020

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI


Conference30th Great Lakes Symposium on VLSI, GLSVLSI 2020
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • General Engineering


  • Benchmarking
  • Computing-in-memory
  • Emerging technologies


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