Embedded vision is an emerging technology area that involves the application of computer vision techniques on embedded systems. Algorithm design and deployment are key components in the embedded vision software development workflow. MATLAB® and Simulink® provide the required tools to accelerate the development workflow from a vision algorithm to embedded code.
Embedded vision applications like autonomous vehicles, smartphone cameras, augmented reality, and medical devices require the end-to-end design workflow provided by MATLAB and Simulink.
By using MATLAB and Simulink in your development workflow, you can:
MATLAB Coder lets you generate C and C++ code from vision algorithms for both desktop systems and embedded hardware. With Embedded Coder, you can expand on MATLAB Coder’s capabilities to achieve hardware-specific optimizations, code traceability between your algorithm and generated code, and SIL and PIL verification. MATLAB Coder also lets you integrate with optimized libraries such as the ARM Compute Library for ARM architectures and MKL-DNN library for Intel CPUs.
HDL Coder enables you to design and generate readable, synthesizable code in VHDL and Verilog for FPGAs and ASICs. Vision HDL Toolbox provides a library of vision algorithms designed for the pixel-streaming architecture required. You can quickly set up and start prototyping with hardware support packages for FPGA-based vision platforms like the Xilinx Zynq and UltraScale platform.
GPU Coder lets you generate optimized CUDA from MATLAB for embedded vision applications, including deep learning. The generated code calls optimized NVIDIA CUDA libraries, including cuFFT, cuBLAS, cuDNN, and TensorRT, and can be used for prototyping on GPUs like the NVIDIA Jetson and Drive platforms.
For more information on these features and capabilities, please follow the link in the description.
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