iMARS: An In-Memory-Computing Architecture for Recommendation Systems Article Swipe
Mengyuan Li
,
Ann Franchesca Laguna
,
Dayane Reis
,
Xunzhao Yin
,
Michael Niemier
,
Xiaobo Sharon Hu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2202.09433
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2202.09433
Recommendation systems (RecSys) suggest items to users by predicting their preferences based on historical data. Typical RecSys handle large embedding tables and many embedding table related operations. The memory size and bandwidth of the conventional computer architecture restrict the performance of RecSys. This work proposes an in-memory-computing (IMC) architecture (iMARS) for accelerating the filtering and ranking stages of deep neural network-based RecSys. iMARS leverages IMC-friendly embedding tables implemented inside a ferroelectric FET based IMC fabric. Circuit-level and system-level evaluation show that \fw achieves 16.8x (713x) end-to-end latency (energy) improvement compared to the GPU counterpart for the MovieLens dataset.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2202.09433
- https://arxiv.org/pdf/2202.09433
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226451692
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4226451692Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2202.09433Digital Object Identifier
- Title
-
iMARS: An In-Memory-Computing Architecture for Recommendation SystemsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-18Full publication date if available
- Authors
-
Mengyuan Li, Ann Franchesca Laguna, Dayane Reis, Xunzhao Yin, Michael Niemier, Xiaobo Sharon HuList of authors in order
- Landing page
-
https://arxiv.org/abs/2202.09433Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2202.09433Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2202.09433Direct OA link when available
- Concepts
-
Computer science, MovieLens, Embedding, Architecture, Parallel computing, Latency (audio), Computer architecture, Artificial intelligence, Recommender system, Table (database), Embedded system, Machine learning, Collaborative filtering, Data mining, Telecommunications, Art, Visual artsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.ferroelectric | 70 |
| abstract_inverted_index.network-based | 60 |
| abstract_inverted_index.Recommendation | 0 |
| abstract_inverted_index.in-memory-computing | 46 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile |