COSIME: FeFET based Associative Memory for In-Memory Cosine Similarity Search
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...
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| Vydáno v: | 2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD) s. 1 - 9 |
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| Jazyk: | angličtina |
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ACM
29.10.2022
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| ISSN: | 1558-2434 |
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| Abstract | 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, a 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 333× and 90.5× 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 47.1× and 98.5× speedup and energy efficiency improvements compared with an GPU implementation. |
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| AbstractList | 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, a 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 333× and 90.5× 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 47.1× and 98.5× speedup and energy efficiency improvements compared with an GPU implementation. |
| Author | Yin, Xunzhao Chen, Haobang Hu, Xiaobo Sharon Kazemi, Arman Imani, Mohsen Zhao, Liang Liu, Che-Kai Zhuo, Cheng Ni, Kai Laguna, Ann Franchesca Niemier, Michael |
| Author_xml | – sequence: 1 givenname: Che-Kai surname: Liu fullname: Liu, Che-Kai organization: Zhejiang University,College of Information Science and Electronic Engineering,Hangzhou,China – sequence: 2 givenname: Haobang surname: Chen fullname: Chen, Haobang organization: Zhejiang University,College of Information Science and Electronic Engineering,Hangzhou,China – sequence: 3 givenname: Mohsen surname: Imani fullname: Imani, Mohsen organization: University of California,Department of Computer Science,Irvine,CA,USA – sequence: 4 givenname: Kai surname: Ni fullname: Ni, Kai organization: Rochester Institute of Technology,Department of Electrical and Microelectronic Engineering,NY,USA – sequence: 5 givenname: Arman surname: Kazemi fullname: Kazemi, Arman organization: University of Notre Dame,Department of Computer Science and Engineering,IN,USA – sequence: 6 givenname: Ann Franchesca surname: Laguna fullname: Laguna, Ann Franchesca organization: De La Salle University,Department of Computer Technology,Manilla,Philippines – sequence: 7 givenname: Michael surname: Niemier fullname: Niemier, Michael organization: University of Notre Dame,Department of Computer Science and Engineering,IN,USA – sequence: 8 givenname: Xiaobo Sharon surname: Hu fullname: Hu, Xiaobo Sharon organization: University of Notre Dame,Department of Computer Science and Engineering,IN,USA – sequence: 9 givenname: Liang surname: Zhao fullname: Zhao, Liang organization: Zhejiang University,College of Information Science and Electronic Engineering,Hangzhou,China – sequence: 10 givenname: Cheng surname: Zhuo fullname: Zhuo, Cheng email: czhuo@zju.edu.cn organization: Zhejiang University,College of Information Science and Electronic Engineering,Hangzhou,China – sequence: 11 givenname: Xunzhao surname: Yin fullname: Yin, Xunzhao email: xzyin1@zju.edu.cn organization: Zhejiang University,College of Information Science and Electronic Engineering,Hangzhou,China |
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| Snippet | 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... |
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| SubjectTerms | Associative memory Energy efficiency Graphics processing units Measurement Nonvolatile memory Support vector machines Throughput |
| Title | COSIME: FeFET based Associative Memory for In-Memory Cosine Similarity Search |
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