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RiBoSOM: Rapid Bacterial Genome Identification Using Self-Organizing Map implemented on the Synchoros SiLago Platform
Royal Inst Technol KTH, Stockholm, Sweden.
Royal Inst Technol KTH, Stockholm, Sweden.
Royal Inst Technol KTH, Stockholm, Sweden.
Indian Inst Technol Delhi, Delhi, India.
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2018 (English)In: 2018 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XVIII), Association for Computing Machinery (ACM), 2018, p. 105-114Conference paper, Published paper (Refereed)
Abstract [en]

Artificial Neural Networks have been applied to many traditional machine learning applications in image and speech processing. More recently, ANNs have caught attention of the bioinformatics community for their ability to not only speed up by not having to assemble genomes but also work with imperfect data set with duplications. ANNs for bioinformatics also have the added attraction of better scaling for massive parallelism compared to traditional bioinformatics algorithms. In this paper, we have adapted Self-organizing Maps for rapid identification of bacterial genomes called BioSOM. BioSOM has been implemented on a design of two coarse grain reconfigurable fabrics customized for dense linear algebra and streaming scratchpad memory respectively. These fabrics are implemented in a novel synchoros VLSI design style that enables composition by abutment. The synchoricity empowers rapid and accurate synthesis from Matlab models to create near ASIC like efficient solution. This platform, called SiLago (Silicon Lego) is benchmarked against a GPU implementation. The SiLago mentation of BioSOMs in four different dimensions, 128, 256, 512 and 1024 Neurons, were trained for two E Coli strains of bacteria with 40K training vectors. The results of SiLago implementation were benchmarked against a GPU GTX 1070 implementation in the CUDA framework. The comparison reveals 4 to 140x speed up and 4 to 5 orders of improvement in energy-delay product compared to implementation on GPU. This extreme efficiency comes with the added benefit of automated generation of GDSII level design from Matlab by using the Synchoros VLSI design style.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2018. p. 105-114
Keywords [en]
Neural networks, Self-Organizing Maps, SiLago, Synchoros VLSI Design, Parallel architecture, 3D DRAM, GPU
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:uu:diva-390239DOI: 10.1145/3229631.3229650ISI: 000475843000013ISBN: 978-1-4503-6494-2 (print)OAI: oai:DiVA.org:uu-390239DiVA, id: diva2:1341186
Conference
18th International Conference on Embedded Computer Systems - Architectures, Modeling, and Simulation (SAMOS), Pythagorion, Greece, July 15-19, 2018
Available from: 2019-08-07 Created: 2019-08-07 Last updated: 2019-08-07Bibliographically approved

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Grabherr, Manfred

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