Model and optimize fixed-point and floating-point algorithms
Fixed-Point Designer™ provides data types and tools for developing fixed-point and single-precision algorithms to optimize performance on embedded hardware. Fixed-Point Designer analyzes your design and proposes data types and attributes such as word length and scaling. You can specify detailed data attributes such as rounding mode and overflow action, and mix single-precision and fixed-point data. You can perform bit-true simulations to observe the impact of limited range and precision without implementing the design on hardware.
Fixed-Point Designer lets you convert double-precision algorithms to single precision or fixed point. You can create and optimize data types that meet numerical accuracy requirements and target hardware constraints. You can determine the range requirements of your design via mathematical analysis or instrumented simulation. Fixed-Point Designer provides apps and tools that guide you through the data conversion process and enable you to compare fixed-point results with floating-point baselines.
Fixed-Point Designer supports C, HDL, and PLC code generation.
Specify the fixed-point properties of your design with application-specific word lengths, binary-point scaling, arbitrary slope and bias scaling, and control details such as rounding and overflow modes.
Emulate target hardware behavior for denormal floating-point numbers, such as flush-to-zero, in simulation and code generation. Simulate limited-precision floating-point with fp16 half-precision data type in MATLAB®.
Instrumentation and Visualization
Collect simulation data and statistics through automatic model-wide instrumentation. Use visualizations to explore and analyze your designs.
Derived Range Analysis
Derive signal ranges based on mathematical analysis of your design and determine the worst-case ranges or edge cases, without having to create fully exhaustive simulation test benches. Using derived ranges, you can make sure your design prevents or handles all possible overflows.
Explore different fixed-point data types and their quantization effects on numerical behavior of your system with a guided workflow. Observe the dynamic range of variables in your design and ensure that the algorithm behaves consistently in floating-point and fixed-point representation after conversion.
Automatically convert a design from double precision to single precision and analyze the effects of limited-precision floating-point representation and quantization in single precision.
Data Type Optimization
Automatically iterate through various fixed-point configurations to choose the optimal heterogenous data types while meeting tolerance constraints on the numerical behavior of your system. The optimization seeks to minimize the total bit-width using fixed-point data types for an efficient design.
Function Approximation and Lookup Table Compression
Approximate mathematically complex functions, such as sqrt, exp, or complex subsystems, with an optimal lookup table. Compress existing lookup tables memory usage by reducing the data points and data types.
Generate Bit-True Code
Ensure bit-true agreement across Model-Based Design from simulation to code generation, including acceleration as well as processor-in-the-loop and software-in-the-loop simulations. Analysis and verification of a fixed-point algorithm are based on bit-true representations.
HDL Optimized Matrix Blocks
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.
Overflow and Precision Loss Detection
Quickly identify, trace, and debug the sources of overflow, precision loss, and wasted range or precision, and compare it against ideal floating-point behavior. Bit-true agreement maximizes many benefits of Model-Based Design, such as the ability to discover issues early in the workflow.
Test Numerical Edge Cases
Generate numerically rich fixed-point and floating-point values to test edge cases, such as values close to boundaries and denormal numbers, for numerical consistency of your algorithms. Generate a combination of signals of varying dimensions or complexity, and integer, floating-point, or fixed-point data types.
Propose data types based on multiple simulation scenarios in the Fixed-Point Tool
Restore model to original design
Lookup Table Optimization
Allow off-curve table values in optimized lookup tables
Data Type Optimization
Specify multiple simulation scenarios for data type optimization
Limited Precision Machine Learning
Quantize and generate fixed-point C/C++ code for a trained SVM model
Test Bit Patterns
Generate simulation inputs to test the full operating bit range for your design
Half-Precision Data Type
Design and simulate half-precision systems in MATLAB