Blind source separation (BSS) is a problem that appears in many research fields. Fast
Independent components analysis (FastICA) is one of the techniques to solve the problem …

Singular value decomposition (SVD) is a central mathematical tool for several emerging
applications in embedded systems, such as multiple-input multiple-output (MIMO) systems …

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) …

Remarkable breakthroughs but daunting complexities of deep learning have aroused
widespread interest in dedicated deep neural network (DNN) acceleration hardware, among …

Matrix decomposition, such as the Singular Value Decomposition (SVD) is an important
compute-intensive task in a wide variety of fields, from radar and simulation to image …

Increasing the degree of freedom for quantum entanglement within tensor networks can
enhance the depiction of the essence in many-body systems. However, this enhancement …

Field-programmable gate arrays (FPGAs) are popular for computational intensive
applications and hardware accelerators recently. But they face limitations in memory …

Efficient FPGA-based floating-point singular value decomposition (SVD) is challenging for its
enormous complexity with the rapid growth of the matrix dimension. Numerous hardware …

Recommender system (RS) is widely used in social networks, computational advertising,
video platforms and many other Internet applications. Most RSs are based on the cloud-to …

Singular value decomposition (SVD) is a central mathematical tool for several emerging
applications in embedded systems, such as multiple-input multiple-output (MIMO) systems …