# fxpopt

Optimize data types of a system

## Description

## Examples

### Optimize Fixed-Point Data Types

This example shows how to optimize the data types used by a system based on specified tolerances.

To begin, open the system for which you want to optimize the data types.

model = 'ex_auto_gain_controller'; sud = 'ex_auto_gain_controller/sud'; open_system(model)

Create an `fxpOptimizationOptions`

object to define constraints and tolerances to meet your design goals. Set the `UseParallel`

property of the `fxpOptimizationOptions`

object to `true`

to run iterations of the optimization in parallel. You can also specify word lengths to allow in your design through the `AllowableWordLengths`

property.

opt = fxpOptimizationOptions('AllowableWordLengths', 10:24, 'UseParallel', true)

opt = fxpOptimizationOptions with properties: MaxIterations: 50 MaxTime: 600 Patience: 10 Verbosity: High AllowableWordLengths: [10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] ObjectiveFunction: BitWidthSum UseParallel: 1 Advanced Options AdvancedOptions: [1×1 DataTypeOptimization.AdvancedFxpOptimizationOptions]

Use the `addTolerance`

method to define tolerances for the differences between the original behavior of the system, and the behavior using the optimized fixed-point data types.

tol = 10e-2; addTolerance(opt, [model '/output_signal'], 1, 'AbsTol', tol);

Use the `fxpopt`

function to run the optimization. The software analyzes ranges of objects in your system under design and the constraints specified in the `fxpOptimizationOptions`

object to apply heterogeneous data types to your system while minimizing total bit width.

result = fxpopt(model, sud, opt);

+ Starting data type optimization... + Checking for unsupported constructs. Starting parallel pool (parpool) using the 'Processes' profile ... 25-Apr-2024 11:22:37: Job Queued. Waiting for parallel pool job with ID 1 to start ... 25-Apr-2024 11:23:38: Job Queued. Waiting for parallel pool job with ID 1 to start ... Connected to parallel pool with 8 workers. + Preprocessing + Modeling the optimization problem - Constructing decision variables + Running the optimization solver - Evaluating new solution: cost 180, does not meet the behavioral constraints. - Evaluating new solution: cost 198, does not meet the behavioral constraints. - Evaluating new solution: cost 216, does not meet the behavioral constraints. - Evaluating new solution: cost 234, does not meet the behavioral constraints. - Evaluating new solution: cost 252, does not meet the behavioral constraints. - Evaluating new solution: cost 270, does not meet the behavioral constraints. - Evaluating new solution: cost 288, does not meet the behavioral constraints. - Evaluating new solution: cost 306, meets the behavioral constraints. - Evaluating new solution: cost 324, meets the behavioral constraints. - Evaluating new solution: cost 342, meets the behavioral constraints. - Evaluating new solution: cost 360, meets the behavioral constraints. - Evaluating new solution: cost 378, meets the behavioral constraints. - Evaluating new solution: cost 396, meets the behavioral constraints. - Evaluating new solution: cost 414, meets the behavioral constraints. - Evaluating new solution: cost 432, meets the behavioral constraints. - Updated best found solution, cost: 306 - Evaluating new solution: cost 304, meets the behavioral constraints. - Evaluating new solution: cost 304, meets the behavioral constraints. - Evaluating new solution: cost 301, meets the behavioral constraints. - Evaluating new solution: cost 305, does not meet the behavioral constraints. - Evaluating new solution: cost 305, meets the behavioral constraints. - Evaluating new solution: cost 301, meets the behavioral constraints. - Evaluating new solution: cost 299, meets the behavioral constraints. - Evaluating new solution: cost 299, meets the behavioral constraints. - Evaluating new solution: cost 296, meets the behavioral constraints. - Evaluating new solution: cost 299, meets the behavioral constraints. - Evaluating new solution: cost 291, meets the behavioral constraints. - Evaluating new solution: cost 296, does not meet the behavioral constraints. - Evaluating new solution: cost 299, meets the behavioral constraints. - Evaluating new solution: cost 300, meets the behavioral constraints. - Evaluating new solution: cost 296, does not meet the behavioral constraints. - Evaluating new solution: cost 301, meets the behavioral constraints. - Evaluating new solution: cost 303, meets the behavioral constraints. - Evaluating new solution: cost 299, meets the behavioral constraints. - Evaluating new solution: cost 304, does not meet the behavioral constraints. - Evaluating new solution: cost 300, meets the behavioral constraints. - Evaluating new solution: cost 295, meets the behavioral constraints. - Evaluating new solution: cost 302, meets the behavioral constraints. - Evaluating new solution: cost 303, does not meet the behavioral constraints. - Evaluating new solution: cost 302, does not meet the behavioral constraints. - Evaluating new solution: cost 298, meets the behavioral constraints. - Evaluating new solution: cost 298, does not meet the behavioral constraints. - Evaluating new solution: cost 277, does not meet the behavioral constraints. - Evaluating new solution: cost 298, does not meet the behavioral constraints. - Evaluating new solution: cost 297, does not meet the behavioral constraints. - Evaluating new solution: cost 303, meets the behavioral constraints. - Evaluating new solution: cost 304, meets the behavioral constraints. - Evaluating new solution: cost 303, meets the behavioral constraints. - Evaluating new solution: cost 305, meets the behavioral constraints. - Evaluating new solution: cost 296, does not meet the behavioral constraints. - Evaluating new solution: cost 301, does not meet the behavioral constraints. - Evaluating new solution: cost 302, meets the behavioral constraints. - Evaluating new solution: cost 299, meets the behavioral constraints. - Evaluating new solution: cost 297, meets the behavioral constraints. - Evaluating new solution: cost 300, does not meet the behavioral constraints. - Evaluating new solution: cost 292, meets the behavioral constraints. - Updated best found solution, cost: 304 - Updated best found solution, cost: 301 - Updated best found solution, cost: 299 - Updated best found solution, cost: 296 - Updated best found solution, cost: 291 - Evaluating new solution: cost 286, meets the behavioral constraints. - Evaluating new solution: cost 276, meets the behavioral constraints. - Evaluating new solution: cost 283, meets the behavioral constraints. - Evaluating new solution: cost 285, does not meet the behavioral constraints. - Evaluating new solution: cost 288, meets the behavioral constraints. - Evaluating new solution: cost 289, meets the behavioral constraints. - Evaluating new solution: cost 288, does not meet the behavioral constraints. - Evaluating new solution: cost 289, meets the behavioral constraints. - Evaluating new solution: cost 290, does not meet the behavioral constraints. - Evaluating new solution: cost 285, meets the behavioral constraints. - Evaluating new solution: cost 281, meets the behavioral constraints. - Evaluating new solution: cost 284, meets the behavioral constraints. - Evaluating new solution: cost 286, meets the behavioral constraints. - Evaluating new solution: cost 278, does not meet the behavioral constraints. - Evaluating new solution: cost 279, does not meet the behavioral constraints. - Evaluating new solution: cost 283, meets the behavioral constraints. - Evaluating new solution: cost 284, meets the behavioral constraints. - Evaluating new solution: cost 283, meets the behavioral constraints. - Evaluating new solution: cost 290, meets the behavioral constraints. - Evaluating new solution: cost 288, meets the behavioral constraints. - Evaluating new solution: cost 283, meets the behavioral constraints. - Evaluating new solution: cost 289, meets the behavioral constraints. - Evaluating new solution: cost 286, meets the behavioral constraints. - Evaluating new solution: cost 288, meets the behavioral constraints. - Evaluating new solution: cost 283, meets the behavioral constraints. - Evaluating new solution: cost 284, meets the behavioral constraints. - Evaluating new solution: cost 282, meets the behavioral constraints. - Evaluating new solution: cost 284, meets the behavioral constraints. - Evaluating new solution: cost 277, does not meet the behavioral constraints. - Evaluating new solution: cost 286, meets the behavioral constraints. - Evaluating new solution: cost 278, does not meet the behavioral constraints. - Evaluating new solution: cost 277, meets the behavioral constraints. - Evaluating new solution: cost 281, meets the behavioral constraints. - Evaluating new solution: cost 290, meets the behavioral constraints. - Evaluating new solution: cost 288, meets the behavioral constraints. - Evaluating new solution: cost 288, meets the behavioral constraints. - Evaluating new solution: cost 287, does not meet the behavioral constraints. - Evaluating new solution: cost 282, meets the behavioral constraints. - Evaluating new solution: cost 286, does not meet the behavioral constraints. - Evaluating new solution: cost 277, meets the behavioral constraints. - Updated best found solution, cost: 286 - Updated best found solution, cost: 276 + Optimization has finished. - Neighborhood search complete. - Maximum number of iterations completed. + Fixed-point implementation that satisfies the behavioral constraints found. The best found solution is applied on the model. - Total cost: 276 - Maximum absolute difference: 0.077478 - Use the explore method of the result to explore the implementation.

Use the `explore`

method of the `OptimizationResult`

object, `result`

, to start the Simulation Data Inspector and explore the design containing the smallest total number of bits while maintaining the numeric tolerances specified in the `opt`

object.

explore(result);

You can revert your model back to its original state using the `revert`

method of the `OptimizationResult`

object.

revert(result);

## Input Arguments

`model`

— Model containing system under design, `sud`

character vector

Name of the model containing the system that you want to optimize.

**Data Types: **`char`

`sud`

— Model or subsystem whose data types you want to optimize

character vector

Model or subsystem whose data types you want to optimize, specified as a character vector containing the path to the system.

**Data Types: **`char`

`options`

— Additional optimization options

`fxpOptimizationOptions`

object

`fxpOptimizationOptions`

object specifying additional options to
use during the data type optimization process.

## Output Arguments

`result`

— Object containing the optimized design

`OptimizationResult`

object

Result of the optimization, returned as an `OptimizationResult`

object. Use the `explore`

method of the object to open the Simulation Data Inspector and view the
behavior of the optimized system. You can also explore other solutions found
during the optimization that may or may not meet the constraints specified
in the `fxpOptimizationOptions`

object,
`options`

.

## Version History

**Introduced in R2018a**

## See Also

### Classes

### Functions

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