Cognitive Correlative Encoding for Genome Sequence Matching in Hyperdimensional System
Pattern matching is one of the key algorithms in identifying and analyzing genomic data. In this paper, we propose HYPERS, a novel framework supporting highly efficient and parallel pattern matching based on HyperDimensional computing (HDC). HYPERS transforms inherent sequential processes of pattern...
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| Vydáno v: | 2021 58th ACM/IEEE Design Automation Conference (DAC) s. 781 - 786 |
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05.12.2021
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| Abstract | Pattern matching is one of the key algorithms in identifying and analyzing genomic data. In this paper, we propose HYPERS, a novel framework supporting highly efficient and parallel pattern matching based on HyperDimensional computing (HDC). HYPERS transforms inherent sequential processes of pattern matching to highly-parallelizable computation tasks using HDC. HYPERS exploits HDC memorization to encode and represent the genome sequences using high-dimensional vectors. Then, it combines the genome sequences to generate an HDC reference library. During the matching, HYPERS performs alignment by exact or approximate similarity check of an encoded query with the HDC reference library. HYPERS functionality is supported by theoretical proof, verified by software implementation, and extensively tested on the existing hardware platform. Our evaluation on FPGA shows that HYPERS provides, on average, 17.5\times speedup and 39.4\times energy efficiency as compared to the state-of-the-art pattern matching tools running on GTX 1080 GPU. |
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| AbstractList | Pattern matching is one of the key algorithms in identifying and analyzing genomic data. In this paper, we propose HYPERS, a novel framework supporting highly efficient and parallel pattern matching based on HyperDimensional computing (HDC). HYPERS transforms inherent sequential processes of pattern matching to highly-parallelizable computation tasks using HDC. HYPERS exploits HDC memorization to encode and represent the genome sequences using high-dimensional vectors. Then, it combines the genome sequences to generate an HDC reference library. During the matching, HYPERS performs alignment by exact or approximate similarity check of an encoded query with the HDC reference library. HYPERS functionality is supported by theoretical proof, verified by software implementation, and extensively tested on the existing hardware platform. Our evaluation on FPGA shows that HYPERS provides, on average, 17.5\times speedup and 39.4\times energy efficiency as compared to the state-of-the-art pattern matching tools running on GTX 1080 GPU. |
| Author | Zou, Zhuowen Sadredini, Elaheh Yin, Xunzhao Poduval, Prathyush Imani, Mohsen |
| Author_xml | – sequence: 1 givenname: Prathyush surname: Poduval fullname: Poduval, Prathyush organization: Indian Institute of Science – sequence: 2 givenname: Zhuowen surname: Zou fullname: Zou, Zhuowen organization: UC San Diego – sequence: 3 givenname: Xunzhao surname: Yin fullname: Yin, Xunzhao organization: Zhejiang University – sequence: 4 givenname: Elaheh surname: Sadredini fullname: Sadredini, Elaheh organization: UC Riverside – sequence: 5 givenname: Mohsen surname: Imani fullname: Imani, Mohsen email: m.imani@uci.edu organization: UC Irvine |
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| Snippet | Pattern matching is one of the key algorithms in identifying and analyzing genomic data. In this paper, we propose HYPERS, a novel framework supporting highly... |
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| SubjectTerms | Genomics Graphics processing units Hardware Libraries Software Transforms |
| Title | Cognitive Correlative Encoding for Genome Sequence Matching in Hyperdimensional System |
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