Optimize and reduce the I/O for algorithms that use large inputs in domains such as image processing, digital signal processing, and radar applications. To optimize the I/O needed for your design, use frame-to-sample conversion, multiple sampling handling, or I/O thresholding.
|Apply neighborhood processing and element-wise operations to an incoming image or matrix for frame-to-sample conversion|
|Apply iterative operation to an incoming image or matrix for frame-to-sample conversion|
|Neighborhood Processing Subsystem||Create algorithm that follows the neighborhood pattern|
- HDL Code Generation from Frame-Based Algorithms
Generate synthesizable HDL code from frame-based algorithms by using the HDL Coder™ frame-to-sample conversion to target sample-based and pixel-based hardware and reduce I/O consumption and prototyping times.
- Optimize Area Usage For Frame-Based Algorithms with Tall Array Inputs
Generate area efficient HDL code from a frame-based algorithm that has input data with significantly more rows than columns.
- Generate HDL Code from Frame-Based Models by Using Neighborhood Modeling Methods
Generate HDL code from frame-based models by using MATLAB Function blocks or the Neighborhood Processing Subsystem block.
- Use Sample-Based Inputs and Frame-Based Inputs in an Algorithm
Generate HDL code from an algorithm that uses both sample-based and frame-based inputs.
- Use Neighborhood, Reduction, and Iterator Patterns with a Frame-Based Model or Function for HDL Code Generation
Generate HDL code from a frame-based design that models neighborhood, reduction, and iterator patterns.