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Designing compact c...
Designing compact convolutional neural network for embedded stereo vision systems
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- Loni, Mohammad (författare)
- Mälardalens högskola,Inbyggda system
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- Majd, A. (författare)
- Åbo Akademi University, Turku, Finland
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- Loni, A. (författare)
- KTH Royal Institute of Technology, Stockholm, Sweden
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- Daneshtalab, Masoud (författare)
- Mälardalens högskola,Inbyggda system
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Sjödin, M. (författare)
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- Troubitsyna, Elena (författare)
- KTH,Skolan för elektroteknik och datavetenskap (EECS),KTH Royal Institute of Technology, Stockholm, Sweden
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- Nolin, Mikael, 1971- (författare)
- Mälardalens högskola,Inbyggda system
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2018
- 2018
- Engelska.
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Ingår i: Proceedings - 2018 IEEE 12th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2018. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538666890 ; , s. 244-251
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Autonomous systems are used in a wide range of domains from indoor utensils to autonomous robot surgeries and self-driving cars. Stereo vision cameras probably are the most flexible sensing way in these systems since they can extract depth, luminance, color, and shape information. However, stereo vision based applications suffer from huge image sizes and computational complexity leading system to higher power consumption. To tackle these challenges, in the first step, GIMME2 stereo vision system [1] is employed. GIMME2 is a high-throughput and cost efficient FPGA-based stereo vision embedded system. In the next step, we present a framework for designing an optimized Deep Convolutional Neural Network (DCNN) for time constraint applications and/or limited resource budget platforms. Our framework tries to automatically generate a highly robust DCNN architecture for image data receiving from stereo vision cameras. Our proposed framework takes advantage of a multi-objective evolutionary optimization approach to design a near-optimal network architecture for both the accuracy and network size objectives. Unlike recent works aiming to generate a highly accurate network, we also considered the network size parameters to build a highly compact architecture. After designing a robust network, our proposed framework maps generated network on a multi/many core heterogeneous System-on-Chip (SoC). In addition, we have integrated our framework to the GIMME2 processing pipeline such that it can also estimate the distance of detected objects. The generated network by our framework offers up to 24x compression rate while losing only 5% accuracy compare to the best result on the CIFAR-10 dataset.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
Nyckelord
- Deep Convolutional Neural Network
- Neural Network Architecture Search
- Neural Processing Unit
- Stereo Vision Systems
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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