RIZK M.

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...

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...

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,...

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...

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 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...