Main Content

Systolic Matrix Solve Using QR Decomposition

Compute value of X in the equation AX = B using QR decomposition

Since R2024a

expand all in page
  • Systolic Matrix Solve Using QR Decomposition block

Libraries:
Fixed-Point Designer HDL Support / Matrices and Linear Algebra / Linear System Solvers

Description

The Systolic Matrix Solve Using QR Decomposition block solves the system of linear equations AX = B using QR decomposition, where A and B are matrices. To compute X = A-1, set B to be the identity matrix.

The systolic implementation to minimizes system latency and increases the throughput. Systolic implementations require more hardware resources than burst or partial-systolic implementations.

Examples

expand all

Open Live Script

This example shows how to use the Systolic Matrix Solve Using QR Decomposition block to compute the solution X of the least-squares matrix equation AX = B.

Solve AX=B Using Economy Size QR Decomposition

The Systolic Matrix Solve Using QR Decomposition block first performs the QR decomposition using the Systolic QR Decomposition block which transforms A in-place to R and B in-place to C = Q'B. The block then solves the transformed system RX = C through back substitution, where QR is the orthogonal-triangular decomposition of A. For more information, see Systolic QR Decomposition.

I/O Interface

This block uses the AMBA AXI handshake protocol on both the input and the output side. On the input side, data transaction occurs when both validIn and ready signals are asserted. On the output side, data transaction occurs when both validOut and readyIn signals are asserted.

In this example, the validIn and readyIn signals remain asserted, which indicates the upstream source and downstream sinks are always available. When all matrices A and B are sent, the Data Handler loops back to the first A and B matrices.

Define Block Parameters

Specify the dimension of the sample matrices A and B, the number of input sample matrices, and the complexity of matrices A and B.

The Systolic Matrix Solve Using QR Decomposition block supports both real and complex inputs. Set the complexity of the input in the block mask accordingly.

m = 3;
n = 3;
p = 1;
numSamples = 3;
Complexity = "complex";

Generate Input A and B Matrices

Use the specified simulation parameters to generate the input matrices A and B.

rng('default');
if strcmp(Complexity,'real')
    A = 2*(rand(m,n,numSamples)-0.5);
    B = 2*(rand(m,p,numSamples)-0.5);
else
    A = complex(2*(rand(m,n,numSamples)-0.5),...
        2*(rand(m,n,numSamples)-0.5));
    B = complex(2*(rand(m,p,numSamples)-0.5),...
        2*(rand(m,p,numSamples)-0.5));
end

Define and Cast to Input Datatype

Specify a data type for the inputs, and cast the inputs to that data type.

DT = "fixed";

For fixed-point data types, define the number of precision bits.

precisionBits = 18;

Cast the inputs to the selected data type.

if strcmp(Complexity,'real')
    T = fixed.realQRMatrixSolveFixedpointTypes(m,n,...
        max(abs(A(:))),max(abs(B(:))),precisionBits);
else
    T = fixed.complexQRMatrixSolveFixedpointTypes(m,n,...
        max(abs(A(:))),max(abs(B(:))),precisionBits);
end
tv = castToDT(A,B,T,DT);

Open Model and Run Simulation

Open the SystolicQRSolverModel model. Configure the model workspace and run the simulation.

model = 'SystolicQRSolverModel';
open_system(model);

fixed.example.setModelWorkspace(model,'A',tv.A,'B',tv.B,...
    'm',m,'n',n,'p',p,'OutputType',tv.OutputType,'numSamples',numSamples);
dutName = [model '/Systolic Matrix Solve Using QR Decomposition'];
set_param(dutName,'complexity',Complexity);
out = sim(model);

Verify Output Solutions

To verify the output solutions, compare the model output to the output of the MATLAB® qr() function.

X = out.X;
for i = 1:numSamples
    relative_error = norm(double(A(:,:,i)*X(:,:,i) - B(:,:,i)))/norm(double(B(:,:,i)))
end
relative_error = 
2.3054e-05

relative_error = 
1.3028e-04

relative_error = 
2.5335e-05

Verify Block Latency

The latency of the Systolic Matrix Solve Using QR Decomposition block depends on the datatype, dimension, and complexity of matrices A and B.

Use the embblk.latency.systolicQRMatrixSolverBlockTiming function to calculate the expected throughput and latency of the block.

[expThroughput,expLatency] = embblk.latency.systolicQRMatrixSolverBlockTiming(tv.A,tv.B)
expThroughput = 
25

expLatency = 
183

Retrieve actual block throughput and latency from the simulation data.

tDataIn = find(out.logsout.get('Input Handshake').Values.Data == 1);
tDataOut = find(out.logsout.get('Output Handshake').Values.Data == 1);
actualthroughput = diff(tDataIn)
actualthroughput = 9×1

    25
    25
    25
    25
    25
    25
    25
    25
    25


actualLatency = tDataOut - tDataIn(1:numSamples)
actualLatency = 3×1

   183
   183
   183

Ports

Input

expand all

Matrix A, specified as a matrix. A is an m-by-n matrix where m ≥ 2 and m ≥ n. If B is single or double, A must be the same data type as B. If A is a fixed-point data type, A must be signed, use binary-point scaling, and have the same word length as B. Slope-bias representation is not supported for fixed-point data types.

Dependencies

Use the Signal type parameter to specify the complexity of the input data.

Data Types: single | double | fixed point
Complex Number Support: Yes

Matrix B, specified as a matrix. B is an m-by-p matrix where m ≥ 2. If A is single or double, B must be the same data type as A. If B is a fixed-point data type, B must be signed, use binary-point scaling, and have the same word length as A. Slope-bias representation is not supported for fixed-point data types.

Dependencies

Use the Signal type parameter to specify the complexity of the input data.

Data Types: single | double | fixed point
Complex Number Support: Yes

Whether inputs are valid, specified as a Boolean scalar. This control signal indicates when the data from the A and B input ports are valid. When this value is 1 (true) and the value at readyIn is 1 (true), the block captures the values on the A and B input ports. When this value is 0 (false), the block ignores the input samples.

Tips

After sending a true validIn signal, there may be some delay before readyIn is set to false. To ensure all data is processed, you must wait until readyIn is set to false before sending another true validIn signal.

Data Types: Boolean

Whether downstream block is ready, specified as a Boolean scalar. This control signal monitors the ready port of the downstream block. When the readyIn value is 1 (true), and the value at validOut is 1 (true), the block outputs data to the downstream block. When the readyIn value is 0 (false), the downstream block is not ready to accept data. The Systolic QR Decomposition block pauses on the output stage and the ready signal remains 0 (false) until the readyIn signal is high.

Data Types: Boolean

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 validIn value is 1 (true), the block begins a new subframe.

Data Types: Boolean

Output

expand all

Matrix X, returned as a matrix.

Data Types: single | double | fixed point
Complex Number Support: Yes

Whether output data is valid, returned as a Boolean scalar. This control signal indicates when the data at output port X is valid. When this value is 1 (true), the block has successfully computed the X matrix. When this value is 0 (false), the output data is not valid.

Data Types: Boolean

Whether block is ready, returned as a Boolean scalar. This control signal that indicates when the block is ready for new input data. When this value is 1 (true) and the validIn value 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.

Tips

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

expand all

To edit block parameters interactively, use the Property Inspector. From the Simulink® Toolstrip, on the Simulation tab, in the Prepare gallery, select Property Inspector.

Number of rows in input matrices A and B, specified as a positive integer-valued scalar.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

To get the block parameter value programmatically, use the get_param function.

Parameter: m
Values: 2 (default) | positive integer-valued scalar
Data Types: char

Number of columns in input matrix A, specified as a positive integer-valued scalar.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

To get the block parameter value programmatically, use the get_param function.

Parameter: n
Values: 2 (default) | positive integer-valued scalar
Data Types: char

Number of columns in input matrix B, specified as a positive integer-valued scalar.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

To get the block parameter value programmatically, use the get_param function.

Parameter: p
Values: 1 (default) | positive integer-valued scalar
Data Types: char

Data type of the output matrix X, specified as fixdt(1,16,0), fixdt(1,18,14), double, single, or as a user-specified data type expression. The type can be specified directly, or expressed as a data type object such as Simulink.NumericType.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

To get the block parameter value programmatically, use the get_param function.

Parameter: OutputType
Values: fixdt(1,18,4) (default) | double | single | fixdt(1,16,0) | <data type expression>
Data Types: char

Complexity of matrices A and B, specified as real or complex.

Programmatic Use

To set the block parameter value programmatically, use the set_param function.

To get the block parameter value programmatically, use the get_param function.

Parameter: complexity
Values: real (default) | complex
Data Types: char

Algorithms

expand all

References

Extended Capabilities

Version History

Introduced in R2024a

See Also

|

Topics