This chapter presents a survey of the existing algorithms and tasks applied for tactile data processing. The presented algorithms and tasks include machine learning, deep learning, feature extraction, and dimensionality reduction. Moreover, this chapter provides guidelines for selecting appropriate hardware platforms for the algorithm’s implementation. The algorithms are compared in terms of computational complexity and hardware implementation requirements. A touch modality classification problem is addressed as a case study: FPGA implementations of two algorithms k-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are detailed and analyzed. Both algorithms provided real-time classification consuming 236mW and 1.14W, respectively. Such results can be improved with the use of approximate computing techniques that provide a trade-off between performance and hardware resources usage. Speedups up to 2× and 3.2× along with 30% and 41% power reduction are obtained for KNN and SVM implementations, respectively.

Efficient algorithms for embedded tactile data processing

Younes H.;Alameh M.;Ibrahim A.;Valle M.
2020

Abstract

This chapter presents a survey of the existing algorithms and tasks applied for tactile data processing. The presented algorithms and tasks include machine learning, deep learning, feature extraction, and dimensionality reduction. Moreover, this chapter provides guidelines for selecting appropriate hardware platforms for the algorithm’s implementation. The algorithms are compared in terms of computational complexity and hardware implementation requirements. A touch modality classification problem is addressed as a case study: FPGA implementations of two algorithms k-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are detailed and analyzed. Both algorithms provided real-time classification consuming 236mW and 1.14W, respectively. Such results can be improved with the use of approximate computing techniques that provide a trade-off between performance and hardware resources usage. Speedups up to 2× and 3.2× along with 30% and 41% power reduction are obtained for KNN and SVM implementations, respectively.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1069122
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