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Prototype Deep Learning Networks on FPGA

Estimate performance of series networks. Profile and retrieve inference results from target devices using MATLAB®

Deep Learning HDL Toolbox™ provides classes to create objects to deploy series deep learning networks to target FPGA and SoC boards. Before deploying deep learning networks onto target FPGA and SoC boards, leverage the methods to estimate the performance and resource utilization of the custom deep learning network. After you deploy the deep learning network, use MATLAB to retrieve the network prediction results from the target FPGA board.


dlhdl.WorkflowConfigure deployment workflow for deep learning neural network
dlhdl.TargetConfigure interface to target board for workflow deployment
dlhdl.SimulatorCreate an object that retrieve intermediate layer results and validate deep learning network prediction accuracy


activations Retrieve intermediate layer results for deployed deep learning network
activations Retrieve intermediate layers results for dlhdl.Simulator object
validateConnectionValidate SSH connection and deployed bitstream
releaseRelease the connection to the target device
predictRun inference on deployed network and profile speed of neural network deployed on specified target device
predictRetrieve prediction results for dlhdl.Simulator object
deploy Deploy the specified neural network to the target FPGA board
compile Compile workflow object
getBuildInfoRetrieve bitstream resource utilization


Prototype Deep Learning Networks on FPGA and SoCs Workflow

Accelerate the prototyping, deployment, design verification, and iteration of your custom deep learning network running on a fixed bitstream by using the dlhdl.Workflow object.

LIBIIO/Ethernet Connection Based Deep Learning Network Deployment

Rapidly deploy deep learning networks to FPGA boards using MATLAB.

Profile Inference Run

Obtain performance parameters of an inference run performed for a pretrained series network and a specified target FPGA board.

Multiple Frame Support

Improve the performance of your deployed deep learning network by using the multiple frame support feature.

Featured Examples