Smart Tactile Sensing Systems Based on Embedded CNN Implementations
Mohamad Alameh,
Yahya Abbass,
Ali Ibrahim,
Maurizio Valle
Affiliations
Mohamad Alameh
Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN)-University of Genoa, via Opera Pia 11a, 16145 Genova, Italy
Yahya Abbass
Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN)-University of Genoa, via Opera Pia 11a, 16145 Genova, Italy
Ali Ibrahim
Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN)-University of Genoa, via Opera Pia 11a, 16145 Genova, Italy
Maurizio Valle
Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN)-University of Genoa, via Opera Pia 11a, 16145 Genova, Italy
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.
