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Approximate Computing Applied to Bacterial Genome Identification using Self-Organizing Maps
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electronics and Embedded systems, Electronic and embedded systems.ORCID iD: 0000-0002-5697-4272
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electronics and Embedded systems, Electronic and embedded systems.ORCID iD: 0000-0003-2396-3590
IIT Delhi, India.
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electronics and Embedded systems, Electronic and embedded systems.ORCID iD: 0000-0003-0565-9376
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2019 (English)In: 2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), IEEE Computer Society, 2019, p. 560-567, article id 8839522Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we explore the design space of a self-organizing map (SOM) used for rapid and accurate identification of bacterial genomes. This is an important health care problem because even in Europe, 70% of prescriptions for antibiotics is wrong. The SOM is trained on Next Generation Sequencing (NGS) data and is able to identify the exact strain of bacteria. This is in contrast to conventional methods that require genome assembly to identify the bacterial strain. SOM has been implemented as an synchoros VLSI design and shown to have 3-4 orders better computational efficiency compared to GPUs. To further lower the energy consumption, we exploit the robustness of SOM by successively lowering the resolution to gain further improvements in efficiency and lower the implementation cost without substantially sacrificing the accuracy. We do an in depth analysis of the reduction in resolution vs. loss in accuracy as the basis for designing a system with the lowest cost and acceptable accuracy using NGS data from samples containing multiple bacteria from the labs of one of the co-authors. The objective of this method is to design a bacterial recognition system for battery operated clinical use where the area, power and performance are of critical importance. We demonstrate that with 39% loss in accuracy in 12 bits and 1% in 16 bit representation can yield significant savings in energy and area.

Place, publisher, year, edition, pages
IEEE Computer Society, 2019. p. 560-567, article id 8839522
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:kth:diva-263799DOI: 10.1109/ISVLSI.2019.00106Scopus ID: 2-s2.0-85072991757ISBN: 978-1-7281-3391-1 (electronic)OAI: oai:DiVA.org:kth-263799DiVA, id: diva2:1370168
Conference
18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019; Miami; United States; 15 July 2019 through 17 July 2019
Note

QC 20191115

Available from: 2019-11-14 Created: 2019-11-14 Last updated: 2019-11-15Bibliographically approved

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