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Authors: Motaz Al-Hami 1 ; Marcin Pietron 2 ; Raul Casas 3 ; Samer Hijazi 3 and Piyush Kaul 3

Affiliations: 1 Hashemite University and Cadence Design Systems, Jordan ; 2 AGH University and Cadence Design Systems, Poland ; 3 Cadence Design Systems, United States

ISBN: 978-989-758-275-2

Keyword(s): Deep Learning, Quantization, Convolutional Neural Networks.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods ; Vision and Perception

Abstract: Nowadays, convolutional neural network (CNN) plays a major role in the embedded computing environment. Ability to enhance the CNN implementation and performance for embedded devices is an urgent demand. Compressing the network layers parameters and outputs into a suitable precision formats would reduce the required storage and computation cycles in embedded devices. Such enhancement can drastically reduce the consumed power and the required resources, and ultimately reduces cost. In this article, we propose several quantization techniques for quantizing several CNN networks. With a minor degradation of the floating-point performance, the presented quantization methods are able to produce a stable performance fixed-point networks. A precise fixed point calculation for coefficients, input/output signals and accumulators are considered in the quantization process.

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Paper citation in several formats:
Al-Hami M., Pietron M., Casas R., Hijazi S. and Kaul P. (2018). Towards a Stable Quantized Convolutional Neural Networks: An Embedded Perspective.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-275-2, pages 573-580. DOI: 10.5220/0006651305730580

@conference{icaart18,
author={Motaz Al-Hami and Marcin Pietron and Raul Casas and Samer Hijazi and Piyush Kaul},
title={Towards a Stable Quantized Convolutional Neural Networks: An Embedded Perspective},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2018},
pages={573-580},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006651305730580},
isbn={978-989-758-275-2},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Towards a Stable Quantized Convolutional Neural Networks: An Embedded Perspective
SN - 978-989-758-275-2
AU - Al-Hami M.
AU - Pietron M.
AU - Casas R.
AU - Hijazi S.
AU - Kaul P.
PY - 2018
SP - 573
EP - 580
DO - 10.5220/0006651305730580

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