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Designing Compact Convolutional Neural Network for Embedded Stereo Vision Systems
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-9704-7117
Åbo Akademi University, Turku, Finland.
KTH Royal Institute of Technology, Stockholm, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
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2018 (English)In: IEEE 12th International Symposium on Embedded Multicore/Many-core Systems-on-Chip MCSoC-2018, 2018, p. 244-251, article id 8540240Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
2018. p. 244-251, article id 8540240
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-40892DOI: 10.1109/MCSoC2018.2018.00049ISI: 000519938300035Scopus ID: 2-s2.0-85059750226ISBN: 9781538666890 (print)OAI: oai:DiVA.org:mdh-40892DiVA, id: diva2:1249068
Conference
IEEE 12th International Symposium on Embedded Multicore/Many-core Systems-on-Chip MCSoC-2018, 12 Sep 2018, Hanoi, Vietnam
Projects
DPAC - Dependable Platforms for Autonomous systems and ControlDeepMaker: Deep Learning Accelerator on Commercial Programmable DevicesAvailable from: 2018-09-18 Created: 2018-09-18 Last updated: 2022-11-08Bibliographically approved
In thesis
1. DeepMaker: Customizing the Architecture of Convolutional Neural Networks for Resource-Constrained Platforms
Open this publication in new window or tab >>DeepMaker: Customizing the Architecture of Convolutional Neural Networks for Resource-Constrained Platforms
2020 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Convolutional Neural Networks (CNNs) suffer from energy-hungry implementation due to requiring huge amounts of computations and significant memory consumption. This problem will be more highlighted by the proliferation of CNNs on resource-constrained platforms in, e.g., embedded systems. In this thesis, we focus on decreasing the computational cost of CNNs in order to be appropriate for resource-constrained platforms. The thesis work proposes two distinct methods to tackle the challenges: optimizing CNN architecture while considering network accuracy and network complexity, and proposing an optimized ternary neural network to compensate the accuracy loss of network quantization methods. We evaluated the impact of our solutions on Commercial-Off-The-Shelf (COTS) platforms where the results show considerable improvement in network accuracy and energy efficiency.

Abstract [sv]

Convolutional Neural Networks (CNNs) lider av energihungriga implementationer på grund av att de kräver enorm beräkningskapacitet och har en betydande minneskonsumtion. Detta problem kommer att framhävas mer när allt fler CNN implementeras på resursbegränsade plattformar i inbyggda datorsystem. I denna uppsats fokuserar vi på att minska resursåtgången för CNN, i termer av behövda beräkningar och behövt minne, för att vara lämplig för resursbegränsade plattformar. Vi föreslår två metoder för att hantera utmaningarna; optimera CNN-arkitektur där man balanserar nätverksnoggrannhet och nätverkskomplexitet, och föreslår ett optimerat ternärt neuralt nätverk för att kompensera noggrannhetsförluster som kan uppstå vid nätverkskvantiseringsmetoder. Vi utvärderade effekterna av våra lösningar på kommersiellt använda plattformar (COTS) där resultaten visar betydande förbättringar i nätverksnoggrannhet och energieffektivitet.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2020
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 299
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-52113 (URN)978-91-7485-490-9 (ISBN)
Presentation
2020-12-04, U2-024 (+ Online/Zoom), Mälardalens högskola, Västerås, 11:30 (English)
Opponent
Supervisors
Projects
DeepMaker: Deep Learning Accelerator on Commercial Programmable DevicesDPAC - Dependable Platforms for Autonomous systems and ControlFAST-ARTS: Fast and Sustainable Analysis Techniques for Advanced Real-Time Systems
Available from: 2020-11-10 Created: 2020-10-29 Last updated: 2020-11-13Bibliographically approved

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Loni, MohammadDaneshtalab, MasoudNolin, Mikael

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