COSIME: FeFET based Associative Memory for In-Memory Cosine Similarity Search Article Swipe
Che-Kai Liu
,
Haobang Chen
,
Mohsen Imani
,
Kai Ni
,
Arman Kazemi
,
Ann Franchesca Laguna
,
Michael Niemier
,
Xiaobo Sharon Hu
,
Liang Zhao
,
Cheng Zhuo
,
Xunzhao Yin
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.12188
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.12188
In a number of machine learning models, an input query is searched across the trained class vectors to find the closest feature class vector in cosine similarity metric. However, performing the cosine similarities between the vectors in Von-Neumann machines involves a large number of multiplications, Euclidean normalizations and division operations, thus incurring heavy hardware energy and latency overheads. Moreover, due to the memory wall problem that presents in the conventional architecture, frequent cosine similarity-based searches (CSSs) over the class vectors requires a lot of data movements, limiting the throughput and efficiency of the system. To overcome the aforementioned challenges, this paper introduces COSIME, an general in-memory associative memory (AM) engine based on the ferroelectric FET (FeFET) device for efficient CSS. By leveraging the one-transistor AND gate function of FeFET devices, current-based translinear analog circuit and winner-take-all (WTA) circuitry, COSIME can realize parallel in-memory CSS across all the entries in a memory block, and output the closest word to the input query in cosine similarity metric. Evaluation results at the array level suggest that the proposed COSIME design achieves 333X and 90.5X latency and energy improvements, respectively, and realizes better classification accuracy when compared with an AM design implementing approximated CSS. The proposed in-memory computing fabric is evaluated for an HDC problem, showcasing that COSIME can achieve on average 47.1X and 98.5X speedup and energy efficiency improvements compared with an GPU implementation.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2207.12188
- https://arxiv.org/pdf/2207.12188
- OA Status
- green
- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4288055046
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4288055046Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2207.12188Digital Object Identifier
- Title
-
COSIME: FeFET based Associative Memory for In-Memory Cosine Similarity SearchWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-25Full publication date if available
- Authors
-
Che-Kai Liu, Haobang Chen, Mohsen Imani, Kai Ni, Arman Kazemi, Ann Franchesca Laguna, Michael Niemier, Xiaobo Sharon Hu, Liang Zhao, Cheng Zhuo, Xunzhao YinList of authors in order
- Landing page
-
https://arxiv.org/abs/2207.12188Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2207.12188Direct 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/2207.12188Direct OA link when available
- Concepts
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Computer science, Content-addressable memory, Parallel computing, Cosine similarity, Latency (audio), Word (group theory), Computer hardware, Computer engineering, Algorithm, Artificial neural network, Artificial intelligence, Pattern recognition (psychology), Mathematics, Telecommunications, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 3, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.when | 191 |
| abstract_inverted_index.with | 193, 227 |
| abstract_inverted_index.word | 156 |
| abstract_inverted_index.(WTA) | 136 |
| abstract_inverted_index.47.1X | 218 |
| abstract_inverted_index.90.5X | 180 |
| abstract_inverted_index.98.5X | 220 |
| abstract_inverted_index.FeFET | 128 |
| abstract_inverted_index.array | 169 |
| abstract_inverted_index.based | 110 |
| abstract_inverted_index.class | 15, 22, 78 |
| abstract_inverted_index.heavy | 52 |
| abstract_inverted_index.input | 8, 159 |
| abstract_inverted_index.large | 41 |
| abstract_inverted_index.level | 170 |
| abstract_inverted_index.paper | 100 |
| abstract_inverted_index.query | 9, 160 |
| abstract_inverted_index.(CSSs) | 75 |
| abstract_inverted_index.COSIME | 138, 175, 213 |
| abstract_inverted_index.across | 12, 144 |
| abstract_inverted_index.analog | 132 |
| abstract_inverted_index.better | 188 |
| abstract_inverted_index.block, | 151 |
| abstract_inverted_index.cosine | 25, 31, 72, 162 |
| abstract_inverted_index.design | 176, 196 |
| abstract_inverted_index.device | 116 |
| abstract_inverted_index.energy | 54, 183, 223 |
| abstract_inverted_index.engine | 109 |
| abstract_inverted_index.fabric | 204 |
| abstract_inverted_index.memory | 62, 107, 150 |
| abstract_inverted_index.number | 2, 42 |
| abstract_inverted_index.output | 153 |
| abstract_inverted_index.vector | 23 |
| abstract_inverted_index.(FeFET) | 115 |
| abstract_inverted_index.COSIME, | 102 |
| abstract_inverted_index.achieve | 215 |
| abstract_inverted_index.average | 217 |
| abstract_inverted_index.between | 33 |
| abstract_inverted_index.circuit | 133 |
| abstract_inverted_index.closest | 20, 155 |
| abstract_inverted_index.entries | 147 |
| abstract_inverted_index.feature | 21 |
| abstract_inverted_index.general | 104 |
| abstract_inverted_index.latency | 56, 181 |
| abstract_inverted_index.machine | 4 |
| abstract_inverted_index.metric. | 27, 164 |
| abstract_inverted_index.models, | 6 |
| abstract_inverted_index.problem | 64 |
| abstract_inverted_index.realize | 140 |
| abstract_inverted_index.results | 166 |
| abstract_inverted_index.speedup | 221 |
| abstract_inverted_index.suggest | 171 |
| abstract_inverted_index.system. | 93 |
| abstract_inverted_index.trained | 14 |
| abstract_inverted_index.vectors | 16, 35, 79 |
| abstract_inverted_index.However, | 28 |
| abstract_inverted_index.accuracy | 190 |
| abstract_inverted_index.achieves | 177 |
| abstract_inverted_index.compared | 192, 226 |
| abstract_inverted_index.devices, | 129 |
| abstract_inverted_index.division | 48 |
| abstract_inverted_index.frequent | 71 |
| abstract_inverted_index.function | 126 |
| abstract_inverted_index.hardware | 53 |
| abstract_inverted_index.involves | 39 |
| abstract_inverted_index.learning | 5 |
| abstract_inverted_index.limiting | 86 |
| abstract_inverted_index.machines | 38 |
| abstract_inverted_index.overcome | 95 |
| abstract_inverted_index.parallel | 141 |
| abstract_inverted_index.presents | 66 |
| abstract_inverted_index.problem, | 210 |
| abstract_inverted_index.proposed | 174, 201 |
| abstract_inverted_index.realizes | 187 |
| abstract_inverted_index.requires | 80 |
| abstract_inverted_index.searched | 11 |
| abstract_inverted_index.searches | 74 |
| abstract_inverted_index.Euclidean | 45 |
| abstract_inverted_index.Moreover, | 58 |
| abstract_inverted_index.computing | 203 |
| abstract_inverted_index.efficient | 118 |
| abstract_inverted_index.evaluated | 206 |
| abstract_inverted_index.in-memory | 105, 142, 202 |
| abstract_inverted_index.incurring | 51 |
| abstract_inverted_index.Evaluation | 165 |
| abstract_inverted_index.circuitry, | 137 |
| abstract_inverted_index.efficiency | 90, 224 |
| abstract_inverted_index.introduces | 101 |
| abstract_inverted_index.leveraging | 121 |
| abstract_inverted_index.movements, | 85 |
| abstract_inverted_index.overheads. | 57 |
| abstract_inverted_index.performing | 29 |
| abstract_inverted_index.showcasing | 211 |
| abstract_inverted_index.similarity | 26, 163 |
| abstract_inverted_index.throughput | 88 |
| abstract_inverted_index.Von-Neumann | 37 |
| abstract_inverted_index.associative | 106 |
| abstract_inverted_index.challenges, | 98 |
| abstract_inverted_index.operations, | 49 |
| abstract_inverted_index.translinear | 131 |
| abstract_inverted_index.approximated | 198 |
| abstract_inverted_index.conventional | 69 |
| abstract_inverted_index.implementing | 197 |
| abstract_inverted_index.improvements | 225 |
| abstract_inverted_index.similarities | 32 |
| abstract_inverted_index.architecture, | 70 |
| abstract_inverted_index.current-based | 130 |
| abstract_inverted_index.ferroelectric | 113 |
| abstract_inverted_index.improvements, | 184 |
| abstract_inverted_index.respectively, | 185 |
| abstract_inverted_index.aforementioned | 97 |
| abstract_inverted_index.classification | 189 |
| abstract_inverted_index.normalizations | 46 |
| abstract_inverted_index.one-transistor | 123 |
| abstract_inverted_index.implementation. | 230 |
| abstract_inverted_index.winner-take-all | 135 |
| abstract_inverted_index.multiplications, | 44 |
| abstract_inverted_index.similarity-based | 73 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 11 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.9100000262260437 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile |