Real Burst Qless QR Decomposition
Libraries:
FixedPoint Designer HDL Support /
Matrices and Linear Algebra /
Matrix Factorizations
Description
The Real Burst Qless QR Decomposition block uses QR decomposition to compute the economy size uppertriangular R factor of the QR decomposition A = QR, where A is a realvalued matrix, without computing Q. The solution to A'Ax = B is x = R\R'\b.
When Regularization parameter is nonzero, the Real Burst Qless QR Decomposition block computes the uppertriangular factor R of the economy size QR decomposition of $$\left[\begin{array}{c}\lambda {I}_{n}\\ A\end{array}\right]$$ where λ is the regularization parameter.
Ports
Input
A(i,:) — Rows of real matrix A
vector
Rows of real matrix A, specified as a vector. A is an mbyn matrix where m ≥ 2 and n ≥ 2. If A is a fixedpoint data type, A must be signed and use binarypoint scaling. Slopebias representation is not supported for fixedpoint data types.
Data Types: single
 double
 fixed point
validIn — Whether inputs are valid
Boolean
scalar
Whether inputs are valid, specified as a Boolean scalar. This control signal
indicates when the data from the A(i,:)
input port is valid. When
this value is 1 (true
) and the value of ready
is 1 (true
), the block captures the values at the
A(i,:)
input port. When this value is 0
(false
), the block ignores the input samples.
After sending a true
validIn
signal, there may be some delay before
ready
is set to false
. To ensure all data is
processed, you must wait until ready
is set to
false
before sending another true
validIn
signal.
Data Types: Boolean
restart — Whether to clear internal states
Boolean
scalar
Whether to clear internal states, specified as a Boolean scalar. When this value
is 1 (true
), the block stops the current calculation and clears all
internal states. When this value is 0 (false
) and the value at
validIn
is 1 (true
), the block begins a new
subframe.
Data Types: Boolean
Output
R(i,:) — Rows of uppertriangular matrix R
scalar  vector
Rows of the economy size QR decomposition matrix R, returned as
a scalar or vector. R is an upper triangular matrix. The size of
the matrix R is min(m,n)byn. The output at R(i,:)
has
the same data type as the input at A(i,:)
.
Data Types: single
 double
 fixed point
validOut — Whether output data is valid
Boolean
scalar
Whether the output data is valid, specified as a Boolean scalar. This control
signal indicates when the data at output port R(i,:)
is valid.
When this value is 1 (true
), the block has successfully computed
the matrix R. When this value is 0 (false
), the
output data is not valid.
Data Types: Boolean
ready — Whether block is ready
Boolean
scalar
Whether the block is ready, returned as a Boolean scalar. This control signal
indicates when the block is ready for new input data. When this value is
1
(true
) and validIn
is
1
(true
), the block accepts input data in the
next time step. When this value is 0
(false
),
the block ignores input data in the next time step.
After sending a true
validIn
signal, there may be some delay before
ready
is set to false
. To ensure all data is
processed, you must wait until ready
is set to
false
before sending another true
validIn
signal.
Data Types: Boolean
Parameters
Number of rows in matrix A — Number of rows in matrix A
4
(default)  positive integervalued scalar
Number of rows in input matrix A, specified as a positive integervalued scalar.
Programmatic Use
Block Parameter:
m 
Type: character vector 
Values: positive integervalued scalar 
Default:
4 
Number of columns in matrix A — Number of columns in matrix A
4
(default)  positive integervalued scalar
Number of columns in input matrix A, specified as a positive integervalued scalar.
Programmatic Use
Block Parameter:
n 
Type: character vector 
Values: positive integervalued scalar 
Default:
4 
Regularization parameter — Regularization parameter
0 (default)  real nonnegative scalar
Regularization parameter, specified as a nonnegative scalar. Small, positive values of the regularization parameter can improve the conditioning of the problem and reduce the variance of the estimates. While biased, the reduced variance of the estimate often results in a smaller mean squared error when compared to leastsquares estimates.
Programmatic Use
Block Parameter:
regularizationParameter 
Type: character vector 
Values: real nonnegative scalar 
Default:
0 
Tips
Use fixed.getQlessQRDecompositionModel(A)
to generate a template model
containing a Real Burst Qless QR Decomposition block for realvalued input
matrix A
.
Algorithms
Choosing the Implementation Method
Partialsystolic implementations prioritize speed of computations over space constraints, while burst implementations prioritize space constraints at the expense of speed of the operations. The following table illustrates the tradeoffs between the implementations available for matrix decompositions and solving systems of linear equations.
Implementation  Ready  Latency  Area 

Systolic  C  O(n)  O(mn^{2}) 
PartialSystolic  C  O(m)  O(n^{2}) 
PartialSystolic with Forgetting Factor  C  O(n)  O(n^{2}) 
Burst  O(n)  O(mn^{2})  O(n) 
Where C is a constant proportional to the word length of the data, m is the number of rows in matrix A, and n is the number of columns in matrix A.
For additional considerations in selecting a block for your application, see Choose a Block for HDLOptimized FixedPoint Matrix Operations.
AMBA AXI Handshake Process
This block uses the AMBA AXI handshake protocol [1]. The valid/ready
handshake process is used to transfer data and control information. This twoway control mechanism allows both the manager and subordinate to control the rate at which information moves between manager and subordinate. A valid
signal indicates when data is available. The ready
signal indicates that the block can accept the data. Transfer of data occurs only when both the valid
and ready
signals are high.
Block Timing
The Burst QR Decomposition blocks accept and process A and B matrices row by row synchronously. After accepting m rows, the block outputs the R and C matrices row by row continuously. The matrices are output from the last row to the first row.
For example, assume that the input A and B matrices
are 3by3. Additionally assume that validIn
asserts before
ready
, meaning that the upstream data source is faster than the QR
decomposition.
In the figure,
A1r1
is the first row of the first A matrix,R1r3
is the third row of the first R matrix, and so on.validIn
toready
— From a successful row input to the block being ready to accept the next row.Last row
validIn
tovalidOut
— From the last row input to the block starting to output the solution.validOut
toready
— From the block starting to output the solution to the block ready to accept the next matrix input.
The Burst Qless QR Decomposition blocks accept and process the matrix A row by row. After accepting m rows, the block outputs the matrix R row by row continuously. The matrix is output from the last row to the first row.
For example, assume that the input A matrix is 3by3. Additionally
assume that validIn
asserts before ready
, meaning that
the upstream data source is faster than the QR decomposition.
In the figure,
A1r1
is the first row of the first A matrix,R1r3
is the third row of the first R matrix, and so on.validIn
toready
— From a successful row input to the block being ready to accept the next row.Last row
validIn
tovalidOut
— From the last row input to the block starting to output the solution.validOut
toready
— From the block starting to output the solution to the block ready to accept the next matrix input.
The following table provides details of the timing for the Burst QR Decomposition blocks.
Block  validIn to ready (cycles)  Last Row validIn to validOut
(cycles)  validOut to ready (cycles) 

Real Burst QR Decomposition  (wl + 5)*min(m,n) + 2  (wl + 5)*min(m,n) + 2  min(m,n) + 1 
Complex Burst QR Decomposition  (wl*2 + 11)*min(m,n) + 2  (wl*2 + 11)*min(m,n) + 2  min(m,n) + 1 
Real Burst Qless QR Decomposition  (wl + 5)*min(m,n) + 2  (wl + 5)*min(m,n) + 2  min(m,n) + 1 
Complex Burst Qless QR Decomposition  (wl*2 + 11)*min(m,n) + 2  (wl*2 + 11)*min(m,n) + 2  min(m,n) + 1 
In the table, m represents the number of rows in matrix A, and n is the number of columns in matrix A. wl represents the word length of A.
If the data type of A is fixed point, then wl is the word length.
If the data type of A is double, then wl is 53.
If the data type of A is single, then wl is 24.
Hardware Resource Utilization
This block supports HDL code generation using the Simulink^{®} HDL Workflow Advisor. For an example, see HDL Code Generation and FPGA Synthesis from Simulink Model (HDL Coder) and Implement Digital Downconverter for FPGA (DSP HDL Toolbox).
This example data was generated by synthesizing the block on a Xilinx^{®} Zynq^{®} UltraScale™ + RFSoC ZCU111 evaluation board. The synthesis tool was Vivado^{®} v.2020.2 (win64).
The following parameters were used for synthesis.
Block parameters:
m = 16
n = 16
Matrix A dimension: 16by16
Input data type:
sfix16_En14
Target frequency: 300 MHz
The following tables show the post placeandroute resource utilization results and timing summary, respectively.
Resource  Usage  Available  Utilization (%) 

CLB LUTs  8006  425280  1.88 
CLB Registers  8286  850560  0.97 
DSPs  0  4272  0.00 
Block RAM Tile  0  1080  0.00 
URAM  0  80  0.00 
Value  

Requirement  3.3333 ns 
Data Path Delay  2.89 ns 
Slack  0.338 ns 
Clock Frequency  333.85 MHz 
References
[1] "AMBA AXI and ACE Protocol Specification Version E." https://developer.arm.com/documentation/ihi0022/e/AMBAAXI3andAXI4ProtocolSpecification/SingleInterfaceRequirements/Basicreadandwritetransactions/Handshakeprocess
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.
Slopebias representation is not supported for fixedpoint data types.
HDL Code Generation
Generate VHDL, Verilog and SystemVerilog code for FPGA and ASIC designs using HDL Coder™.
HDL Coder™ provides additional configuration options that affect HDL implementation and synthesized logic.
This block has one default HDL architecture.
General  

ConstrainedOutputPipeline  Number of registers to place at
the outputs by moving existing delays within your design. Distributed
pipelining does not redistribute these registers. The default is

InputPipeline  Number of input pipeline stages
to insert in the generated code. Distributed pipelining and constrained
output pipelining can move these registers. The default is

OutputPipeline  Number of output pipeline stages
to insert in the generated code. Distributed pipelining and constrained
output pipelining can move these registers. The default is

Supports fixedpoint data types only.
Version History
Introduced in R2020aR2022a: Support for Tikhonov regularization parameter
The Real Burst Qless QR Decomposition block now supports the Tikhonov Regularization parameter.
MATLAB 명령
다음 MATLAB 명령에 해당하는 링크를 클릭했습니다.
명령을 실행하려면 MATLAB 명령 창에 입력하십시오. 웹 브라우저는 MATLAB 명령을 지원하지 않습니다.
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