Academia.eduAcademia.edu

Figure 3 – uploaded by IJSTE - International Journal of Science Technology and Engineering

See full PDFdownloadDownload figure
Fig. 3: Realization of V ectorized A chitecture using System Generator block sets

Figure 3 Realization of V ectorized A chitecture using System Generator block sets

Related Figures (4)

A, Fixed Point Architecture
A, Fixed Point Architecture
Fig. 1: Realization of Fixed Point Achitecture using System Generator block sets
Fig. 1: Realization of Fixed Point Achitecture using System Generator block sets
Fig. 2: Realization of Iterative A chitecture using System Generator block sets An area efficient low power implementation of QR algorithm using Householder method can be performed using iterative architecture. The proposed design realized for an iteration using system generator block sets uses same hardware blocks to calculate each step in the algorithm to reduce the area of the overall design. The main focus is to implement unified division, square root and inverse square root block into QR algorithm by focusing on fixed iteration division, square root and inverse square root analysis. The iterative architecture is realized under Simulink environment by using System Generator block sets from Xilinx ISE Design Suite 14.4.
Fig. 2: Realization of Iterative A chitecture using System Generator block sets An area efficient low power implementation of QR algorithm using Householder method can be performed using iterative architecture. The proposed design realized for an iteration using system generator block sets uses same hardware blocks to calculate each step in the algorithm to reduce the area of the overall design. The main focus is to implement unified division, square root and inverse square root block into QR algorithm by focusing on fixed iteration division, square root and inverse square root analysis. The iterative architecture is realized under Simulink environment by using System Generator block sets from Xilinx ISE Design Suite 14.4.
\ gebraic method/approximation method is based on floating point unit for optimized matrix factorization based on Householde ransformation. A LAC (Linear Algebra Core) achieves orders of magnitude better efficiency in power and area consumptio1 compared to conventional general purpose architectures. It is specifically optimized to perform rank-1 updates that form th nner kernels of parallel matrix multiplication. Fig 4 shows the realization of architecture for one iteration with input vector [5 6 nd obtained the results for one iteration at MATLAB workspace. Fig. 4: Realization of Algebraic/ Approximation methods using System Generator block sets
\ gebraic method/approximation method is based on floating point unit for optimized matrix factorization based on Householde ransformation. A LAC (Linear Algebra Core) achieves orders of magnitude better efficiency in power and area consumptio1 compared to conventional general purpose architectures. It is specifically optimized to perform rank-1 updates that form th nner kernels of parallel matrix multiplication. Fig 4 shows the realization of architecture for one iteration with input vector [5 6 nd obtained the results for one iteration at MATLAB workspace. Fig. 4: Realization of Algebraic/ Approximation methods using System Generator block sets
Related topics:
Matrix InversionSystem GeneratorHardware Architectures and ImplementationsHouseholder TransformationQR Decomposition
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved