VALLE M.

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...

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The development of embedded and real-time systems for Machine Learning data processing is challenging (e.g. IoT). Low latency, low power consumption and reduced hardware complexity should be the characteristics of such systems. Considering prosthetic applications, which are error-tolerant, a technique that tunes the precision of operands and...

Approximate computing has emerged as a promising approach to energy-efficient design of digital systems in many domains such as digital signal processing, robotics, and machine learning. Numerous studies report that employing different data formats in Deep Neural Networks (DNNs), the dominant Machine Learning approach, could allow substantial...

This paper proposes an embedded tactile sensory feedback system for the upper-limb prosthesis. The feedback system delivers tactile information extracted from tactile sensors to the user through electrocutaneous stimulation. The proposed system has been tested experimentally on three healthy subjects. Results demonstrate the correct feedback of...

Efficiency, real-time operation and low-power consumption are the main requirements of embedded Machine Learning implementations. This paper proposes an approach for applying Algorithmic level Approximate Computing Techniques (ACTs) on two supervised machine learning algorithms. The proposed approach has been validated in two different...

Developing portable autonomous systems is highly requested for numerous application domains such as Internet of Things (IoT), wearable devices, and biomedical applications. Portable systems usually contain autonomous and networked sensors; each sensor hosts multiple input channels (e.g. tactile) closely coupled to embedded computing unit and...

Approximate Computing Techniques (ACT) are promising solutions towards the achieve-ment of reduced energy, time latency and hardware size for embedded implementations of machine learning algorithms. In this paper, we present the first FPGA implementation of an approximate tensorial Support Vector Machine (SVM) classifier with algorithmic level...

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...

Approximate computing circuits are considered as a promising solution to reduce the power consumption in embedded data processing. This paper proposes an FPGA implementation for an approximate multiplier based on inexact adder circuits. The performance of the proposed multiplier is evaluated by comparing the power consumption, the accuracy of...

Approximate computing techniques offer a promising solution to reduce the hardware complexity and power consumption imposed when embedding machine learning algorithms. The reduction comes at the cost of some performance degradation. This paper presents an approximate machine learning classifier for touch modality recognition. Experimental...

This paper presents a novel hardware architecture of the Tensorial Support Vector Machine (TSVM) based on Shallow Neural Networks (NN) for the Single Value Decomposition (SVD) computation. The proposed NN achieves a comparable Mean Squared Error and Cosine Similarity to the widely used one-sided Jacobi algorithm. When implemented on an FPGA,...

As the technology moves towards more human-like bionic limbs it is necessary to develop a feedback system that provides active touch feedback to a user of a prosthetic hand. Most of the contemporary sensory substitution methods comprise simple position and force sensors combined with few discrete stimulation units,and hence they are...

Tactile data processing and analysis is still essentially an open challenge. In this framework, we demonstrate a method to achieve touch modality classification using pre-trained convolutional neural networks (CNNs). The 3D tensorial tactile data generated by real human interactions on an electronic skin (E-Skin) are transformed into 2D images....

This paper presents a novel architecture for the Singular Value Decomposition (SVD) algorithm. The architecture embraces the reductions offered by the use of Approximate Computing (AxC) as a trade-off between complexity and accuracy. A shallow Neural Network (NN) consisting of three layers is used to compute the SVD of an input matrix, offering...