Accelerating Basecalling with Dataflow Computing

Master thesis (2020)

Authors

L.C. Vermond Electrical Engineering, Mathematics and Computer Science

Contributors

G. Gaydadjiev Computer Engineering - EEMCS (mentor)
W.A. Serdijn Bio-Electronics - EEMCS (mentor)
M. Popov Intelligent Electrical Power Grids - EEMCS (mentor)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2020 Lukas Vermond
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Published Date

01-10-2020

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In an effort to make DNA sequencing more accessible and affordable, Oxford Nanopore Technologies developed the MinION: A portable cellphone sized DNA sequencing device. Translating the information from this device to a nucleotide sequence is called basecalling, and is done with the aid of artificial neural networks. In this thesis, we accelerate a neural network using the concept of Dataflow programming. The result is a complete basecalling application that relies on an FPGA based platform to run the compute intensive parts. We achieve up to 1.51x speedup over a high-end server with two Intel Xeon Processors, with a rate of 57,040 bases per second. In addition, our implementation uses up to 90.27\% less energy compared to the original implementation.