Fixed-Point Designer
Model and optimize fixed-point and floating-point algorithms
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Fixed-Point Designer provides data types and tools for optimizing and implementing fixed-point and floating-point algorithms on embedded hardware. It includes fixed-point and floating-point data types and target-specific numeric settings. With Fixed-Point Designer you can perform target-aware simulation that is bit-true for fixed point. You can then test and debug quantization effects such as overflows and precision loss before implementing the design on hardware.
Fixed-Point Designer provides apps and tools for analyzing double-precision algorithms and converting them to reduced-precision floating point or fixed point. Optimization tools enable you to select data types that meet your numerical accuracy requirements and target hardware constraints. For efficient implementation you can replace computationally expensive design constructs with hardware-optimal patterns such as compressed lookup tables. Additionally, Fixed-Point Designer enables the conversion of learnable parameters in machine learning and deep learning models to fixed-point data types for optimized performance.
Production C and HDL code can be generated directly from your fixed- and floating-point optimized models.
Fixed-Point Modeling
Evaluate performance tradeoffs on numerical precision by simulating fixed-point algorithms with application-specific word lengths, binary-point, or arbitrary slope and bias scaling. Control details such as rounding and overflow modes.
Bit-True Code Generation
Maintain bit-true agreement between simulation and generated code for reduced-precision designs, ensuring high-fidelity algorithm deployment.
Fixed-Point AI Models
Quantize learnable parameters of machine learning models and deep neural networks to fixed-point in preparation for deployment to resource-constrained devices.
Catch Errors Early
Quickly identify and debug sources of overflow, precision loss, and wasted range or precision. Resolve issues with numerical behavior earlier in the Model-Based Design workflow, lowering development costs.
Automated Data Typing
Improve the numerical efficiency of your designs with automated fixed-point and floating-point data typing. Explore quantization effects on numerical behavior with guided conversion workflows.
Embedded Floating-Point
Automatically convert designs from double to single and half-precision for enhanced efficiency in embedded environments. Emulate flush-to-zero behavior for denormal numbers.
Cross-Product Support
Integrate fixed-point numbers across your designs, from modeling to final deployment. Leverage built-in fixed-point support for signal, audio processing, and communications workflows.
HDL-Optimized Matrix Blocks
Access a Fixed-Point HDL Library of Simulink blocks that model design patterns for systems of linear equations and core matrix operations, such as QR decomposition, for hardware-efficient implementation on FPGAs. Generate HDL code with HDL Coder.
Lookup Table Compression
Approximate mathematically complex functions or complex subsystems with an optimal lookup table. Compress existing lookup tables to reduce memory usage by optimizing data points and data types.
Product Resources:
“MATLAB, MATLAB Coder, and Fixed-Point Designer enabled our small team to develop a complex real-time signal processing algorithm, optimize it to reduce power and memory requirements, accelerate embedded code implementation, and perform the rigorous testing required for medical device validation.”
Fixed-Point Designer FAQs
Fixed-Point Designer provides data types and tools for optimizing and implementing fixed-point and floating-point algorithms on embedded hardware, including target-specific numeric settings and bit-true simulation capabilities.
It provides apps and tools for analyzing double-precision algorithms and converting them to reduced-precision floating point or fixed point, with optimization tools to select data types that meet numerical accuracy requirements and target hardware constraints.
Yes, production C and HDL code can be generated directly from your fixed- and floating-point optimized models using compatible code generation products.
You can perform target-aware, bit-true simulation to test and debug quantization effects such as overflows and precision loss before implementing the design on hardware.
Yes, Fixed-Point Designer enables the conversion of learnable parameters in machine learning and deep learning models to fixed-point data types for optimized performance on resource-constrained devices.
Fixed-Point Designer supports automatic conversion of designs from double to single and half-precision for enhanced efficiency in embedded environments and can emulate flush-to-zero behavior for denormal numbers.
You can approximate mathematically complex functions or subsystems with optimal lookup tables and compress existing lookup tables to reduce memory usage by optimizing data points and data types.
Yes, it includes a Fixed-Point HDL Library of Simulink blocks that model hardware-efficient design patterns for systems of linear equations and core matrix operations like QR decomposition for FPGA implementation.
Conversion workflows can identify unsupported functions and replace them using supported implementations or lookup table approximations.
Lookup Table Optimizer is an app that approximates functions or blocks with optimized lookup tables, supporting fixed‑point and floating‑point data types.
Data Type Agent recommends fixed‑point data types for specific Fixed‑Point Designer block libraries, using analytic models and interactive visualization.
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