July 29, 2021 Report Open Access

Radojković, Petar; Carpenter, Paul; Esmaili-Dokht, Pouya; Cimadomo, Rémy; Charles, Henri-Pierre; Sebastian, Abu; Amato, Paolo

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  <dc:creator>Radojković, Petar</dc:creator>
  <dc:creator>Carpenter, Paul</dc:creator>
  <dc:creator>Esmaili-Dokht, Pouya</dc:creator>
  <dc:creator>Cimadomo, Rémy</dc:creator>
  <dc:creator>Charles, Henri-Pierre</dc:creator>
  <dc:creator>Sebastian, Abu</dc:creator>
  <dc:creator>Amato, Paolo</dc:creator>
  <dc:date>2021-07-29</dc:date>
  <dc:description>Decades after being initially explored in the 1970s, Processing in Memory (PIM) is currently experiencing a renaissance.  By moving part of the computation to the memory devices, PIM addresses a fundamental issue in the design of modern computing systems, the mismatch between the von Neumann architecture and the requirements of important data-centric applications. A number of industrial prototypes and products are under development or already available in the marketplace, and these devices show the potential for cost-effective and energy-efficient acceleration of HPC, AI and data analytics workloads. This paper reviews the reasons for the renewed interest in PIM and surveys industrial prototypes and products, discussing their technological readiness.

Wide adoption of PIM in production, however, depends on our ability to create an ecosystem to drive and coordinate innovations and co-design across the whole stack. European companies and research centres should be involved in all aspects, from technology, hardware, system software and programming environment, to updating of the algorithm and application. In this paper, we identify the main challenges that must be addressed and we provide guidelines to prioritise the research efforts and funding. We aim to help make PIM a reality in production HPC, AI and data analytics.</dc:description>
  <dc:description>This work was supported by the by the Spanish Government (contract PID2019-107255GB), Generalitat de Catalunya (contracts 2017-SGR-1328 and 2017-SGR-1414), and the European Union's Horizon 2020 research and innovation programme under grant agreements No 955606 (DEEP-SEA) and No 682675 (Projected Memristor European Research Council grant). Paul Carpenter holds the Ramon y Cajal fellowship under contracts RYC2018-025628-I of the Ministry of Economy and Competitiveness of Spain. This work was also supported by the Collaboration Agreement between Micron Technology, Inc. and BSC. The authors wish to thank Xavier Martorell from BSC for his technical support, and Manolis Marazakis and André Brinkmann for their feedback.</dc:description>
  <dc:identifier>https://zenodo.org/record/4767489</dc:identifier>
  <dc:identifier>10.5281/zenodo.4767489</dc:identifier>
  <dc:identifier>oai:zenodo.org:4767489</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/682675/</dc:relation>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/955606/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.4767488</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/etp4hpc</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Processing in Memory</dc:subject>
  <dc:subject>PIM</dc:subject>
  <dc:title>Processing in Memory: The Tipping Point</dc:title>
  <dc:type>info:eu-repo/semantics/report</dc:type>
  <dc:type>publication-report</dc:type>
</oai_dc:dc>