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HPC"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Modern recommendation systems integrate graph convolution neural networks (GCN) for enhancing embedding representation. Compared with widely deployed neural network-based models, the extra message propagation layer of GCN-based recommendation is featured with extensive computations and irregular memory access. However, architecture designs for prevailing deep neural network recommendation models assume simple pooling in the embedding layer. ReRAM-based GCN accelerators are specialized for graph-related operations. However, they are designed for general graphs, while GCN-based recommendation models mainly operate on the user-item graph. In this paper, we proposed a resistive random accessed memory (ReRAM) based processing-in-memory (PIM) accelerator, ReGCNR, for GCN-based recommendation. ReGCNR is featured with three key innovations. First, we exploit the 3-dimensional (3-D) stacked heterogeneous ReRAM to fit with the large-size embedding table and user-item graph. Then, we propose a joint degree mapping schema that maximizes the efficiency of the execution pipeline. After that, ReGCNR assembles a well-coordinated pipeline and hardware scheduling design to boost overall system performance. Results show that ReGCNR outperforms GPU by 69.83<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\times$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u00d7<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and 56.67<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\times$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u00d7<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> in terms of average speedup and energy saving, respectively. In addition, ReGCNR outperforms state-of-the-art ReRAM-based solutions by 11.13<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\times$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u00d7<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> speedups and 7.22<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\times$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u00d7<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> energy savings on average.<\/jats:p>","DOI":"10.1007\/s42514-024-00180-4","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T20:02:11Z","timestamp":1709150531000},"page":"150-163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A heterogeneous 3-D stacked PIM accelerator for GCN-based recommender systems"],"prefix":"10.1007","volume":"6","author":[{"given":"Xinyang","family":"Shen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3927-1102","authenticated-orcid":false,"given":"Yu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofei","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hai","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"180_CR1","doi-asserted-by":"crossref","unstructured":"Arka, A.I., Doppa, J.R., Pande, P.P., Joardar, B.K., Chakrabarty, K.: ReGraphX: NoC-enabled 3D heterogeneous ReRAM architecture for training graph neural networks. 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