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DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University.
Shiraz University of Technology, Shiraz, Iran.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
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2020 (English)In: Microprocessors and microsystems, ISSN 0141-9331, E-ISSN 1872-9436, Vol. 73, article id 102989Article in journal (Refereed) Published
Abstract [en]

Deep Neural Networks (DNNs) are compute-intensive learning models with growing applicability in a wide range of domains. Due to their computational complexity, DNNs benefit from implementations that utilize custom hardware accelerators to meet performance and response time as well as classification accuracy constraints. In this paper, we propose DeepMaker framework that aims to automatically design a set of highly robust DNN architectures for embedded devices as the closest processing unit to the sensors. DeepMaker explores and prunes the design space to find improved neural architectures. Our proposed framework takes advantage of a multi-objective evolutionary approach that exploits a pruned design space inspired by a dense architecture. DeepMaker considers the accuracy along with the network size factor as two objectives to build a highly optimized network fitting with limited computational resource budgets while delivers an acceptable accuracy level. In comparison with the best result on the CIFAR-10 dataset, a generated network by DeepMaker presents up to a 26.4x compression rate while loses only 4% accuracy. Besides, DeepMaker maps the generated CNN on the programmable commodity devices, including ARM Processor, High-Performance CPU, GPU, and FPGA. 

Place, publisher, year, edition, pages
Elsevier B.V. , 2020. Vol. 73, article id 102989
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-46792DOI: 10.1016/j.micpro.2020.102989ISI: 000520940000032Scopus ID: 2-s2.0-85077516447OAI: oai:DiVA.org:mdh-46792DiVA, id: diva2:1388110
Available from: 2020-01-23 Created: 2020-01-23 Last updated: 2020-04-09Bibliographically approved

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Loni, MohammadDaneshtalab, MasoudSjödin, Mikael

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