Complex Partial-Systolic Matrix Solve Using Q-less QR Decomposition

Compute the value of X in the equation A'AX = B for complex-valued matrices using Q-less QR decomposition

Since R2020b

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

Description

The Complex Partial-Systolic Matrix Solve Using Q-less QR Decomposition block solves the system of linear equations, A'AX = B, using Q-less QR decomposition, where A and B are complex-valued matrices.

When Regularization parameter is nonzero, the Complex Partial-Systolic Matrix Solve Using Q-less QR Decomposition block solves the matrix equation

$${\left[\begin{array}{c}\lambda I_n\\ A\end{array}\right]}^{\text{'}}\cdot \left[\begin{array}{c}\lambda I_n\\ A\end{array}\right]X=\left({\lambda }^2{I}_n+A\text{'}A\right)X=B$$

where λ is the regularization parameter, A is an m-by-n matrix, and I_n = eye(n).

Examples

Implement Hardware-Efficient Complex Partial-Systolic Matrix Solve Using Q-less QR Decomposition

How to use the Complex Partial-Systolic Matrix Solve Using Q-less QR Decomposition block.

• Open Script

Implement Hardware-Efficient Complex Partial-Systolic Matrix Solve Using Q-less QR Decomposition with Tikhonov Regularization

Use the Complex Partial-Systolic Matrix Solve Using QR Decomposition block to solve the regularized least-squares matrix equation

• Open Script

Algorithms to Determine Fixed-Point Types for Complex Q-less QR Matrix Solve A'AX=B

Derivation of algorithms for determining fixed-point types for complex Q-less QR matrix solve.

• Open Live Script

Determine Fixed-Point Types for Complex Q-less QR Matrix Solve A'AX=B

Use fixed.complexQlessQRFixedpointTypes to determine fixed-point types for computation of the complex least-squares matrix equation.

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Determine Fixed-Point Types for Complex Q-less QR Matrix Solve with Tikhonov Regularization

Use the fixed.complexQlessQRMatrixSolveFixedpointTypes function to analytically determine fixed-point types for the solution of the complex least-squares matrix equation

• Open Live Script

Ports

Input

A(i,:) — Rows of matrix A

vector

Rows of matrix A, specified as a vector. 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.

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

B — Matrix B

vector

Matrix B, specified as a vector. B is an n-by-p matrix where n ≥ 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.

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

validInA — Whether input A is valid

Boolean scalar

Whether input A is 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 at readyA 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 validInA signal, there may be some delay before readyA is set to false. To ensure all data is processed, you must wait until readyA is set to false before sending another true validInA signal.

Data Types: Boolean

validInB — Whether input B is valid

Boolean scalar

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

After sending a true validInB signal, there may be some delay before readyB is set to false. To ensure all data is processed, you must wait until readyB is set to false before sending another true validInB 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 validIn value is 1 (true), the block begins a new subframe.

Data Types: Boolean

Output

X — Matrix X

matrix | vector

Matrix X, returned as a vector or matrix.

Data Types: single | double | fixed point

validOut — Whether output data is valid

Boolean scalar

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

Data Types: Boolean

readyA — Whether block is ready for input A

Boolean scalar

Whether the block is ready for input A, 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 validInA 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.

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

Data Types: Boolean

readyB — Whether block is ready for input B

Boolean scalar

Whether the block is ready for input B, 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 validInB 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.

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

Data Types: Boolean

Parameters

Number of rows in matrix A — Number of rows in matrix A

4 (default) | positive integer-valued scalar

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

Programmatic Use

Block Parameter: m

Type: character vector

Values: positive integer-valued scalar

Default: 4

Number of columns in matrix A and rows in matrix B — Number of columns in matrix A and rows in matrix B

4 (default) | positive integer-valued scalar

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

Programmatic Use

Block Parameter: n

Type: character vector

Values: positive integer-valued scalar

Default: 4

Number of columns in matrix B — Number of columns in matrix B

1 (default) | positive integer-valued scalar

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

Programmatic Use

Block Parameter: p

Type: character vector

Values: positive integer-valued scalar

Default: 1

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 least-squares estimates.

Programmatic Use

Block Parameter: regularizationParameter

Type: character vector

Values: real nonnegative scalar

Default: 0

Output datatype — Data type of output matrix X

fixdt(1,18,14) (default) | double | single | fixdt(1,16,0) | <data type expression>

Data type of the output matrix X, specified as fixdt(1,18,14), double, single, fixdt(1,16,0), 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

Block Parameter: OutputType

Type: character vector

Values: 'fixdt(1,18,14)' | 'double' | 'single' | 'fixdt(1,16,0)' | '<data type expression>'

Default: 'fixdt(1,18,14)'

Algorithms

Choosing the Implementation Method

Systolic 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	Throughput	Latency	Area
Systolic	C	O(n)	O(mn2)
Partial-Systolic	C	O(m)	O(n2)
Partial-Systolic with Forgetting Factor	C	O(n)	O(n2)
Burst	O(n)	O(mn)	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 HDL-Optimized Fixed-Point 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 two-way 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.

Synchronous vs Asynchronous Implementation

The Matrix Solve Using QR Decomposition blocks operate synchronously. These blocks first decompose the input A and B matrices into R and C matrices using a QR decomposition block. Then, a back substitute block computes RX = C. The input A and B matrices propagate through the system in parallel, in a synchronized way.

The Matrix Solve Using Q-less QR Decomposition blocks operate asynchronously. First, Q-less QR decomposition is performed on the input A matrix and the resulting R matrix is put into a buffer. Then, a forward backward substitution block uses the input B matrix and the buffered R matrix to compute R'RX = B. Because the R and B matrices are stored separately in buffers, the upstream Q-less QR decomposition block and the downstream Forward Backward Substitute block can run independently. The Forward Backward Substitute block starts processing when the first R and B matrices are available. Then it runs continuously using the latest buffered R and B matrices, regardless of the status of the Q-less QR Decomposition block. For example, if the upstream block stops providing A and B matrices, the Forward Backward Substitute block continues to generate the same output using the last pair of R and B matrices.

The Burst (Asynchronous) Matrix Solve Using Q-less QR Decomposition blocks are available in both synchronous and asynchronous operation variants, as denoted by the block name.

Block Timing

The Partial-Systolic Matrix Solve Using Q-less QR Decomposition blocks accept matrix A row-by-row and matrix B as a single vector. After accepting the first valid pair of A and B matrices, the block outputs the X matrices row by row continuously.

For example, assume that the input A matrix is 3-by-3. 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, A1r2 is the second row of the first A matrix, and so on.

• validIn to ready — From a successful A row input to the block being ready to accept the next row.

• validOut to validOut — Because the Forward Backward Substitution block runs continuously, it generates output at a constant rate. This is the delay between two adjacent valid outputs.

• Last row validIn to validOut — From the last m^th row input to the block starting to output the solution.

• This block is always ready to accept B matrices, so readyB is always asserted.

The following table provides details of the timing for the Complex Partial-Systolic Matrix Solve Using Q-less QR Decomposition block. Latency depends on the size of matrix A and the data types of the A and B matrices. In the table:

• n is the number of columns in matrix A.

• wl represents the word length of the input data in matrix A.

Input Data Type	validIn to ready (cycles)	validOut to validOut (cycles)	Last Row validIn to validOut (cycles)
Fixed point fi	wl + 9	4*n2 + 25*n + 5 + 2*n*wl + 2*n*nextpow2(wl)	4*n2 + 25*n + 5 + 2*n*wl + 2*n*nextpow2(wl) + (wl + 7.5)*2*n + 2
Scaled double fi	wl + 9	4*n2 + 23*n + 5 + 2*n*wl	4*n2 + 38*n + 4*n*wl + 7
double	62	4*n2 + 21*n + 5	4*n2 + 142*n + 7
single	33	4*n2 + 21*n + 5	4*n2 + 84*n + 7

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).

In R2022b: The following tables show the post place-and-route resource utilization results and timing summary, respectively.

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

  • p = 1

  • Matrix A dimension: 16-by-16

  • Matrix B dimension: 16-by-1

• Input data type: sfix16_En14

• Target frequency: 250 MHz

Resource	Usage	Available	Utilization (%)
CLB LUTs	300675	425280	70.70
CLB Registers	260811	850560	30.66
DSPs	12	4272	0.28
Block RAM Tile	0	1080	0.00
URAM	0	80	0.00

	Value
Requirement	4 ns
Data Path Delay	3.954 ns
Slack	0.029 ns
Clock Frequency	251.83 MHz

References

[1] "AMBA AXI and ACE Protocol Specification Version E." https://developer.arm.com/documentation/ihi0022/e/AMBA-AXI3-and-AXI4-Protocol-Specification/Single-Interface-Requirements/Basic-read-and-write-transactions/Handshake-process

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using Simulink® Coder™.

Slope-bias representation is not supported for fixed-point 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.

HDL Architecture

This block has one default HDL architecture.

HDL Block Properties

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 0. For more details, see ConstrainedOutputPipeline (HDL Coder).
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 0. For more details, see InputPipeline (HDL Coder).
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 0. For more details, see OutputPipeline (HDL Coder).

Restrictions

Supports fixed-point data types only.

Version History

Introduced in R2020b

R2023a: Smart unrolling for improved resource utilization

This block depends on a partial-systolic QR decomposition block. Since 23a, when you update the diagram, the loop which composes the partial-systolic pipeline in the QR decomposition block is unrolled. This updated internal architecture removes dead operations in simulation and generated code, thus requiring fewer hardware resources. This block simulates with clock and bit-true fidelity with respect to library versions of these blocks in previous releases.

R2022a: Support for Tikhonov regularization parameter

The Complex Partial-Systolic Matrix Solve Using Q-less QR Decomposition block now supports the Tikhonov Regularization parameter.

R2021a: Reduced HDL resource utilization

This block now has an improved algorithm to reduce resource utilization on hardware-constrained target platforms.