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ADONN: Adaptive design of optimized deep neural networks for embedded systems
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-7586-0409
2018 (English)In: Proceedings - 21st Euromicro Conference on Digital System Design, DSD 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 397-404Conference paper, Published paper (Refereed)
Abstract [en]

Nowadays, many modern applications, e.g. autonomous system, and cloud data services need to capture and process a big amount of raw data at runtime that ultimately necessitates a high-performance computing model. Deep Neural Network (DNN) has already revealed its learning capabilities in runtime data processing for modern applications. However, DNNs are becoming more deep sophisticated models for gaining higher accuracy which require a remarkable computing capacity. Considering high-performance cloud infrastructure as a supplier of required computational throughput is often not feasible. Instead, we intend to find a near-sensor processing solution which will lower the need for network bandwidth and increase privacy and power efficiency, as well as guaranteeing worst-case response-times. Toward this goal, we introduce ADONN framework, which aims to automatically design a highly robust DNN architecture for embedded devices as the closest processing unit to the sensors. ADONN adroitly searches the design space to find improved neural architectures. Our proposed framework takes advantage of a multi-objective evolutionary approach, which exploits a pruned design space inspired by a dense architecture. Unlike recent works that mainly have tried to generate highly accurate networks, ADONN also considers the network size factor as the second objective to build a highly optimized network fitting with limited computational resource budgets while delivers comparable accuracy level. In comparison with the best result on CIFAR-10 dataset, a generated network by ADONN presents up to 26.4 compression rate while loses only 4% accuracy. In addition, ADONN maps the generated DNN on the commodity programmable devices including ARM Processor, High-Performance CPU, GPU, and FPGA.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018. p. 397-404
Keywords [en]
Approximation Computing, Multi-Objective Optimization, Neural Architectural Search, Neural Processing Unit, Budget control, Data handling, Distributed computer systems, Embedded systems, Evolutionary algorithms, Integrated circuit design, Multiobjective optimization, Network architecture, Neural networks, Systems analysis, Cloud infrastructures, Computational resources, High-performance computing models, Learning capabilities, Multi-objective evolutionary, Neural-processing, Deep neural networks
National Category
Computer Engineering Communication Systems Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-41390DOI: 10.1109/DSD.2018.00074Scopus ID: 2-s2.0-85056450132ISBN: 9781538673768 (print)OAI: oai:DiVA.org:mdh-41390DiVA, id: diva2:1362220
Conference
21st Euromicro Conference on Digital System Design, DSD 2018, 29 August 2018 through 31 August 2018
Available from: 2019-10-18 Created: 2019-10-18 Last updated: 2019-10-18

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Loni, MohammadDaneshtalab, MasoudSjödin, Mikael

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