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...

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Binary Neural Networks (BNN) have been proposed to address the computational complexity and memory requirements of Convolutional Neural Networks (CNN). However, in most of the applications, BNNs suffer from severe accuracy loss due to the 1-bit quantization. In this paper, a Mixed-Precision Binary Weight Network (MP-BWN) is proposed as a...

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...

An experimental analog VLSI neural chip is presented. The chip integrates 4 neurons and 32 synapses organized in a Single Layer Perceptron architecture with 8 inputs and 4 outputs. The neural computational units (neurons and synapses) feature on-chip learning capabilities following the Back-Propagation algorithm. The operation of the neural...

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...

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...

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...

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...

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 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,...