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Deep Learning on a Xilinx FPGA with MATLAB Code
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FPGA-based hardware is a good fit for deep learning inferencing on embedded devices because they deliver low latency and power consumption. Early prototyping is essential to developing a deep learning network that can be efficiently deployed to an FPGA.
See how Deep Learning HDL Toolbox™ automates FPGA prototyping of deep learning networks directly from MATLAB®. With a few lines of MATLAB code, you can deploy to and run inferencing on a Xilinx® ZCU102 FPGA board. This direct connection allows you to run deep learning inferencing on the FPGA as part of your application in MATLAB, so you can converge more quickly on a network that meets your system requirements.
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© 2020 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See how Deep Learning HDL Toolbox™ automates FPGA prototyping of deep learning networks directly from MATLAB®. With a few lines of MATLAB code, you can deploy to and run inferencing on a Xilinx® ZCU102 FPGA board. This direct connection allows you to run deep learning inferencing on the FPGA as part of your application in MATLAB, so you can converge more quickly on a network that meets your system requirements.
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© 2020 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
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