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Deep Learning INT8 Quantization

Calibrate, validate, and deploy quantized pretrained series deep learning networks

Increase throughput, reduce resource utilization, and deploy larger networks onto smaller target boards by quantizing your deep learning networks.

After calibrating your pretrained series network by collecting instrumentation data, quantize your series network and validate the accuracy of your quantized network. Once the quantized network has been validated, generate code for and deploy the quantized network.


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dlquantizationOptionsOptions for quantizing a trained deep neural network (Since R2020a)
dlquantizerQuantize a deep neural network to 8-bit scaled integer data types (Since R2020a)
calibrateSimulate and collect ranges of a deep neural network (Since R2020a)
validateQuantize and validate a deep neural network (Since R2020a)
quantizeQuantize deep neural network (Since R2022a)
dlhdl.WorkflowConfigure deployment workflow for deep learning neural network (Since R2020b)
dlhdl.TargetConfigure interface to target board for workflow deployment (Since R2020b)
dlhdl.SimulatorCreate an object that retrieves intermediate layer results and validate deep learning network prediction accuracy (Since R2021b)
compile Compile workflow object (Since R2020b)
deploy Deploy the specified neural network to the target FPGA board (Since R2020b)
predictPredict responses by using deployed network (Since R2020b)
predictRetrieve prediction results for dlhdl.Simulator object (Since R2021b)
releaseRelease the connection to the target device (Since R2020b)
validateConnectionValidate SSH connection and deployed bitstream (Since R2020b)


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Quantization Workflow