Simulink 中的数据类型优化

高效设计的定点数据类型优化

数据类型优化自动循环访问各种定点配置，以选择最佳异构数据类型，同时满足对系统数值行为的容差约束。优化力图最小化目标函数，如总位宽或总运算符计数，以期通过定点数据类型来实现高效设计。

您可以在命令行中使用 fxpopt 函数或在定点工具中使用优化的定点转换工作流来优化数据类型。

工具

定点工具	Convert a floating-point model to a fixed-point model

fxpopt Optimize data types of a system

`fxpOptimizationOptions`	Specify options for data type optimization
`OptimizationSolution`	Optimized fixed-point implementation of system
`OptimizationResult`	Result after optimizing fixed-point system

Data Type Conversion Overview
Convert data types in your model to fixed point in one of three ways.
Best Practices for Fixed-Point Conversion Workflow
Use best practices when converting a model to fixed point.
Specify Behavioral Constraints
Use signal tolerances and model verification blocks to verify behavior of fixed-point implementation.
Optimize Fixed-Point Data Types for a System
Optimize data types in a system based on specified tolerances.
Optimize the Fixed-Point Data Types of a System Using the Fixed-Point Tool
Use the Fixed-Point Tool to optimize the data types of a system using multiple simulation scenarios.
Optimize Data Types Using Multiple Simulation Scenarios
Define multiple simulation scenarios for range collection and verification.
Perform Data Type Optimization with Custom Behavioral Constraints
Use Model Verification blocks to specify custom behavioral constraints for data type optimization with fxpopt.
Model Configuration Changes Made During Data Type Optimization
Changes made to model configuration parameters during data type optimization with fxpopt.
Use Design Evolution Manager with the Fixed-Point Tool
Example showing how to manage different design versions while converting a model from floating point to fixed point.

Troubleshoot blocks that are not supported for fixed-point conversion.

Troubleshoot errors thrown during data type optimization using the fxpopt function.

Follow best practices and avoid unsupported MATLAB^® Function block features.

Use the addSpecification method to achieve better mapping for product and gain blocks.

Convert a neural network to fixed point using fixed-point optimization and lookup table optimization.

Convert a Restricted Boltzmann Machine (RBM) algorithm to an embedded-efficient version.