Benchmarking and Profiling for Pre-Alignment Filtering in Memory

doi: 10.4121/94b9bfa7-42d5-418e-a947-a02c244921a6.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/94b9bfa7-42d5-418e-a947-a02c244921a6
Datacite citation style:
Shahroodi, Taha; Miao, Michael (2023): Benchmarking and Profiling for Pre-Alignment Filtering in Memory. Version 1. 4TU.ResearchData. dataset.
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Benchmarking and Profiling for Pre-Alignment Filtering in Memory used for the evaluation of the papers titled RattlesnakeJake: A Fast and Accurate Pre-Alignment Filter Suitable for Computation-in-Memory, SieveMem : A Computation-in-Memory Architecture for Fast and Accurate Pre-Alignment , and the M.Sc. thesis of Michael Miao at QCE department. 

  • 2023-09-04 first online, published, posted
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TU Delft, Faculty of Engineering, Mathematics and Computer Science (EEMCS/EWI), Department of Quantum & Computer Engineering (QCE)
Eidgenössische Technische Hochschule (ETH) Zürich

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