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Proceedings ArticleDOI

Realization of area efficient QR factorization using unified division, square root, and inverse square root hardware

TLDR
Unified hardware architecture for fast, area efficient QR factorization based on the Householder transformation is presented and the design and implementation of the proposed hardware is presented with synthesis results based on FPGA hardware.
Abstract
The QR factorization is used in many signal processing and communication applications such as echo cancellation, adaptive beamforming and multiple-inputmultiple- output (MIMO) systems. However, division, square root and inverse square root operations required by the QR algorithm are very difficult to implement because they are computationally slow and area-consuming arithmetic operations. This paper presents unified hardware architecture for fast, area efficient QR factorization based on the Householder transformation. Newton-Raphson, and Goldschmidt algorithms are used for fast division, square root and inverse square root blocks. By using a unified architecture, area and power requirements for QR factorization are reduced without decreasing overall speed. The design and implementation of the proposed hardware is presented with synthesis results based on FPGA hardware.

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Citations
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Proceedings ArticleDOI

FPGA implementation of fast QR decomposition based on givens rotation

TL;DR: An improved fixed-point hardware design of QR decomposition, specifically optimized for Xilinx FPGAs is introduced, and a Givens Rotation algorithm is implemented by using a folded systolic array and the CORDIC algorithm, making this very suitable for high-speed FPGA or ASIC designs.
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Stress Recognition from Heterogeneous Data

TL;DR: This thesis proposes an approach based on a SVM classifier (Support Vector Machine) and shows that the reaction time can be used to estimate the level of stress of an individual in addition or not to the physiological signals.
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Algorithm, Architecture, and Floating-Point Unit Codesign of a Matrix Factorization Accelerator

TL;DR: This paper examines the mapping of algorithms encountered when solving dense linear systems and linear least-squares problems to a custom Linear Algebra Processor and exposes the benefits of redesigning floating point units and their surrounding data-paths to support these complicated operations.
Proceedings ArticleDOI

Floating Point Architecture Extensions for Optimized Matrix Factorization

TL;DR: This paper examines the mapping of algorithms encountered when solving dense linear systems and linear least-squares problems to a custom Linear Algebra Processor and exposes the benefits of redesigning floating point units and their surrounding data-paths to support these complicated operations.
Journal ArticleDOI

NoC-Based FPGA Acceleration for Monte Carlo Simulations with Applications to SPECT Imaging

TL;DR: This paper presents a compute architecture for accelerating Monte Carlo simulations based on the Network-on-Chip (NOC) paradigm for on-chip communication and demonstrates through the complete implementation of a Monte Carlo-based image reconstruction algorithm for Single-Photon Emission Computed Tomography (SPECT) imaging that this complex problem can be accelerated by two orders of magnitude.
References
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