# embblk.latency.systolicQRMatrixSolverBlockTiming

Compute throughput and latency of Systolic Matrix Solve Using QR Decomposition block

Since R2024a

## Syntax

``[throughput,latency] = embblk.latency.systolicQRMatrixSolverBlockTiming(A,B)``

## Description

example

````[throughput,latency] = embblk.latency.systolicQRMatrixSolverBlockTiming(A,B)` returns the `throughput` and `latency` of the Systolic Matrix Solve Using QR Decomposition block. This computation depends on the data type, complexity, and dimension of the input matrices `A,B`.```

## Examples

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Define input matrices `A` and `B`. Compute the latency and throughput of the Systolic Matrix Solve Using QR Decomposition block for these inputs.

```A = fi(complex(randn(4,4),randn(4,4)),1,17,10); B = fi(complex(randn(4,1),randn(4,1)),1,17,10); [throughput,latency] = embblk.latency.systolicQRMatrixSolverBlockTiming(A,B)```
```throughput = 20 latency = 190```

## Input Arguments

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Input matrices A and B to the Systolic Matrix Solve Using QR Decomposition block, specified as matrices.

Data Types: `single` | `double` | `fi`
Complex Number Support: Yes

## Output Arguments

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Throughput of the Systolic Matrix Solve Using QR Decomposition block, returned as a scalar. Throughput is defined as the number of clock cycles between adjacent ready signals.

Latency of the Systolic Matrix Solve Using QR Decomposition block, returned as a scalar. Latency is defined as the number of clock cycles between the input and the corresponding output.

## Version History

Introduced in R2024a