High Level Synthesis FPGA Implementation of the Jacobi Algorithm to Solve the Eigen Problem

We present a hardware implementation of the Jacobi algorithm to compute the eigenvalue decomposition (EVD). The computation of eigenvalues and eigenvectors has many applications where real time processing is required, and thus hardware implementations are often mandatory. Some of these implementations have been carried out with field programmable gate array (FPGA) devices using low level register transfer level (RTL) languages. In the present study, we used the Xilinx Vivado HLS tool to develop a high level synthesis (HLS) design and evaluated different hardware architectures. After analyzing the design for different input matrix sizes and various hardware configurations, we compared it with the results of other studies reported in the literature, concluding that although resource usage may be higher when HLS tools are used, the design performance is equal to or better than low level hardware designs.

[1]  Ruyun Ma,et al.  On a System Modelling a Population with Two Age Groups , 2014 .

[2]  Anindya Sundar Dhar,et al.  CORDIC Architectures: A Survey , 2010, VLSI Design.

[3]  Jürgen Götze,et al.  Parallel Jacobi EVD Methods on Integrated Circuits , 2014, VLSI Design.

[4]  J. Faires,et al.  Numerical Methods , 2002 .

[5]  S. N. Atluri,et al.  Application of Nondimensional Dynamic Influence Function Method for Eigenmode Analysis of Two-Dimensional Acoustic Cavities , 2014 .

[6]  Gene H. Golub,et al.  Matrix computations , 1983 .

[7]  Gumersindo Verdú,et al.  Resolution of the Generalized Eigenvalue Problem in the Neutron Diffusion Equation Discretized by the Finite Volume Method , 2014 .

[8]  B. K. Bhagavan,et al.  Generalized eigenvalue problems: Lanczos algorithm with a recursive partitioning method , 2000 .

[9]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[10]  José Luis Lázaro,et al.  Novel HW Architecture Based on FPGAs Oriented to Solve the Eigen Problem , 2008, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[11]  Yang Liu,et al.  Hardware architectures for eigenvalue computation of real symmetric matrices , 2009, IET Comput. Digit. Tech..

[12]  Jack E. Volder The CORDIC Trigonometric Computing Technique , 1959, IRE Trans. Electron. Comput..

[13]  Shaochuan Wu,et al.  Convergence of Gossip Algorithms for Consensus in Wireless Sensor Networks with Intermittent Links and Mobile Nodes , 2014 .

[14]  Slim Choura,et al.  Confinement of Vibrations in Variable-Geometry Nonlinear Flexible Beam , 2014 .

[15]  Yougen Xu,et al.  A FPGA-based implementation of MUSIC for centrosymmetric circular array , 2008, 2008 9th International Conference on Signal Processing.

[16]  William Ford,et al.  Numerical Linear Algebra with Applications: Using MATLAB , 2014 .

[17]  Abbes Amira,et al.  PCA IP-core for gas applications on the heterogenous zynq platform , 2013, 2013 25th International Conference on Microelectronics (ICM).

[18]  R. Brent,et al.  The Solution of Singular-Value and Symmetric Eigenvalue Problems on Multiprocessor Arrays , 1985 .

[19]  Dianne P. O'Leary,et al.  Parallel QR factorization by Householder and modified Gram-Schmidt algorithms , 1990, Parallel Comput..

[20]  Franklin T. Luk,et al.  Computation Of The Generalized Singular Value Decomposition Using Mesh-Connected Processors , 1983, Optics & Photonics.

[21]  Álvaro Hernández,et al.  Using PCA in time-of-flight vectors for reflector recognition and 3-D localization , 2005, IEEE Transactions on Robotics.

[22]  S. Azou,et al.  An algorithm for extremal eigenvectors computation of Hermitian matrices and its FPGA implementation , 2013, 2013 IEEE 56th International Midwest Symposium on Circuits and Systems (MWSCAS).

[23]  Joseph R. Cavallaro,et al.  CORDIC arithmetic for an SVD processor , 1987, 1987 IEEE 8th Symposium on Computer Arithmetic (ARITH).

[24]  A. Booth Numerical Methods , 1957, Nature.

[25]  Wei Deng,et al.  Investigation of the Dynamic Stability of Microgrid , 2014, IEEE Transactions on Power Systems.

[26]  Li Ping,et al.  Improved PCA with optimized sensor locations for process monitoring and fault diagnosis , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).