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Open AccessArticle

Smart Tactile Sensing Systems Based on Embedded CNN Implementations

1
Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN)-University of Genoa, via Opera Pia 11a, 16145 Genova, Italy
2
Department of Electrical and Electronics Engineering, Lebanese International University (LIU), Beirut 1105, Lebanon
*
Author to whom correspondence should be addressed.
Micromachines 2020, 11(1), 103; https://doi.org/10.3390/mi11010103
Received: 1 December 2019 / Revised: 4 January 2020 / Accepted: 15 January 2020 / Published: 18 January 2020
(This article belongs to the Special Issue Tactile Sensing Technology and Systems)
Embedding machine learning methods into the data decoding units may enable the extraction of complex information making the tactile sensing systems intelligent. This paper presents and compares the implementations of a convolutional neural network model for tactile data decoding on various hardware platforms. Experimental results show comparable classification accuracy of 90.88% for Model 3, overcoming similar state-of-the-art solutions in terms of time inference. The proposed implementation achieves a time inference of 1.2 ms while consuming around 900 μ J. Such an embedded implementation of intelligent tactile data decoding algorithms enables tactile sensing systems in different application domains such as robotics and prosthetic devices. View Full-Text
Keywords: tactile sensing systems; embedding intelligence; convolutional neural network tactile sensing systems; embedding intelligence; convolutional neural network
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MDPI and ACS Style

Alameh, M.; Abbass, Y.; Ibrahim, A.; Valle, M. Smart Tactile Sensing Systems Based on Embedded CNN Implementations. Micromachines 2020, 11, 103.

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