Within digital hardware, numbers are represented as either fixed-point or floating-point data types. For both of these data types, word sizes are fixed at a set number of bits. However, the dynamic range of fixed-point values is much less than floating-point values with equivalent word sizes. While floating-point processors can greatly simplify the real-time implementation of a system, and effectively approximate real-world numbers, fixed-point processors carry numerous other benefits. Fixed-point processors are generally smaller, consuming less power. They also require less memory and less processor time to perform.
To simulate a model that uses fixed-point numbers, you must install the Fixed-Point Designer™ product. You do not need the Fixed-Point Designer product to edit a model containing fixed-point blocks, or to specify fixed-point data types.
|Optimize fixed-point approximation of nonlinear function by interpolating lookup table data points|
|Modify breakpoints of lookup table to have even spacing|
|Implement 1-D lookup table|
|Plot fixed-point approximation function for lookup table|
|Set property for each fixed-point block in subsystem|
|Exponent that gives best precision for fixed-point representation of value|
|Determine maximum precision available for fixed-point representation of value|
|Convert number to nearest value representable by specified fixed-point data type|
If you do not have Fixed-Point Designer, you can still inspect and use fixed-point models that others share with you.
Interactively apply data types, such as integer, fixed-point, and enumerated types, to data items in a model.
Inspect and use an existing fixed-point model when you do not have Fixed-Point Designer.
If you do not have Fixed-Point Designer, you can work with a model containing Simulink® blocks with fixed-point settings by turning off fixed-point instrumentation and setting data type override to scaled doubles.
In computer memory, an item of fixed-point data is stored as an integer. To interpret the data as a real-world number, the computer applies a mathematical scaling to the integer. The scaling is fixed, which means it cannot change during execution.
Fixed-point designs can perform faster and consume fewer computing resources than floating-point designs.
Examine the interaction between the scaling that you apply to fixed-point data, the precision with which the data can represent real-world values, and the range of real-world values that the data can represent.
Apply fixed-point data types to data in Simulink models and to data in MATLAB® code.