Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Chen, Hanning | - |
dc.contributor.author | Kim, Yeseong | - |
dc.contributor.author | Sadredini, Elaheh | - |
dc.contributor.author | Gupta, Saransh | - |
dc.contributor.author | Latapie, Hugo | - |
dc.contributor.author | Imani, Mohsen | - |
dc.date.accessioned | 2024-02-08T23:10:12Z | - |
dc.date.available | 2024-02-08T23:10:12Z | - |
dc.date.created | 2024-01-02 | - |
dc.date.issued | 2023-10-16 | - |
dc.identifier.isbn | 9798350325997 | - |
dc.identifier.issn | 2324-8432 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/47907 | - |
dc.description.abstract | In this paper, we propose a Hyper-Dimensional genome analysis platform. Instead of working with original sequences, our method maps the genome sequences into high-dimensional space and performs sequence matching with simple and parallel similarity searches. At the algorithm level, we revisit the sequence searching with brain-like memorization that Hyper-Dimensional computing natively supports. Instead of working on the original data, we map all data points into high-dimensional space, enabling the main sequence searching operations to process in a hardware-friendly way. We accordingly design a density-aware FPGA implementation. Our solution searches the similarity of an encoded query and large-scale genome library through different chunks. We exploit the holographic representation of patterns to stop search operations on libraries with a lower chance of a match. This translates our computation from dense to highly sparse just after a few chuck-based searches. Our evaluation shows that our accelerator can provide 46× speedup and 188× energy efficiency improvement compared to a state-of-the-art GPU implementation. Results show that our accelerator achieves up to 3440.6 GCUPS using a single Xilinx Alveo U280 board. © 2023 IEEE. | - |
dc.language | English | - |
dc.publisher | International Federation for Information Processing Technical Committee | - |
dc.relation.ispartof | IEEE/IFIP International Conference on VLSI and System-on-Chip, VLSI-SoC | - |
dc.title | Sparsity Controllable Hyperdimensional Computing for Genome Sequence Matching Acceleration | - |
dc.type | Conference Paper | - |
dc.identifier.doi | 10.1109/VLSI-SoC57769.2023.10321874 | - |
dc.identifier.wosid | 001108827200008 | - |
dc.identifier.scopusid | 2-s2.0-85179848318 | - |
dc.identifier.bibliographicCitation | IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2023, pp.44 - 49 | - |
dc.identifier.url | https://easychair.org/smart-program/VLSI-SoC2023/2023-10-16.html#talk:235290 | - |
dc.citation.conferenceDate | 2023-10-16 | - |
dc.citation.conferencePlace | AR | - |
dc.citation.conferencePlace | Dubai, U ARAB EMIRATES | - |
dc.citation.endPage | 49 | - |
dc.citation.startPage | 44 | - |
dc.citation.title | IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2023 | - |
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