deep learning int8 quantization -凯发k8网页登录

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.

functions

quantization and validation

	options for quantizing a trained deep neural network
	quantize a deep neural network to 8-bit scaled integer data types
	simulate and collect ranges of a deep neural network
	quantize and validate a deep neural network

code generation and deployment

	configure deployment workflow for deep learning neural network
	configure interface to target board for workflow deployment
	create an object that retrieves intermediate layer results and validate deep learning network prediction accuracy
	compile workflow object
	deploy the specified neural network to the target fpga board
`predict`	predict responses by using deployed network
	retrieve prediction results for `dlhdl.simulator` object
	release the connection to the target device
	validate ssh connection and deployed bitstream

topics

get started

supported networks, layers, boards, and tools
pretrained deep learning networks and network layers for which code can be generated by deep learning hdl toolbox™.
quantization of deep neural networks
understand effects of quantization and how to visualize dynamic ranges of network convolution layers.

quantization workflow

products required for the quantization of deep learning networks.
simulate your pretrained series network and collect the dynamic range of weights and biases.
quantize and validate your pretrained series deep learning network.
generate code and deploy your quantized pretrained series deep learning network.

featured examples

classify images on fpga using quantized neural network

use deep learning hdl toolbox™ to deploy a quantized deep convolutional neural network (cnn) to an fpga. in the example you use the pretrained resnet-18 cnn to perform transfer learning and quantization. you then deploy the quantized network and use matlab ® to retrieve the prediction results.