Prototype Deep Learning Networks on FPGA

Create, compile, and deploy a dlhdl.Workflow object that has Logo Recognition Network as the network object using the Deep Learning HDL Toolbox™ Support Package for Xilinx FPGA and SoC. Use MATLAB® to retrieve the prediction results from the target device.

Create, compile, and deploy a lane detection convolutional neural network (CNN) to an FPGA, and use MATLAB® to retrieve the prediction results.

Create, compile, and deploy a dlhdl.Workflow object with ResNet-18 as the network object by using the Deep Learning HDL Toolbox™ Support Package for Xilinx FPGA and SoC. Use MATLAB® to retrieve the prediction results from the target device. ResNet-18 is a pretrained convolutional neural network that has been trained on over a million images and can classify images into 1000 object categories (such as keyboard, coffee, mug, pencil,and many animals). You can also use VGG-19 and DarkNet-19 as the network objects.

Train, compile, and deploy a dlhdl.Workflow object that has ResNet-18 neural network to an FPGA and use MATLAB® to retrieve the prediction results.

Deploy a custom trained series network to detect defects in objects such as hexagon nuts. The custom networks were trained by using transfer learning. Transfer learning is commonly used in deep learning applications. You can take a pretrained network and use it as a starting point to learn a new task. Fine-tuning a network with transfer learning is usually much faster and easier than training a network with randomly initialized weights from scratch. You can quickly transfer learned features to a new task using a smaller number of training signals. This example uses two trained series networks, trainedDefNet.mat and trainedBlemDetNet.mat.

Deploy a custom trained series network to detect pedestrians and bicyclists based on their micro-Doppler signatures. This network is taken from the Pedestrian and Bicyclist Classification Using Deep Learning example from the Phased Array Toolbox. For more details on network training and input data, see Pedestrian and Bicyclist Classification Using Deep Learning.

Feed an image to a convolutional neural network and display the activations of the different layers of the network. Examine the activations and discover which features the network learns by comparing areas of activation to the original image. Channels in earlier layers learn simple features like color and edges, while channels in the deeper layers learn complex features. Identifying features in this way can help you understand what the network has learned.

Typical series classification networks include a sequence of convolution layers followed by one or more fully connected layers. Recent research results indicate that better performance is achieved for feature extraction and recognition by using the convolution layer activations directly, instead of those from the subsequent fully connected layers.

Train and deploy a you only look once (YOLO) v2 object detector.

Classify human electrocardiogram (ECG) signals by deploying a transfer learning trained SqueezeNet network trainedSN to a Xilinx Zynq Ultrascale+ ZCU102 board.

Rapidly prototype your custom deep learning network and bitstream by visualizing intermediate layer activation results and verifying prediction accuracy without target hardware by emulating the network and bitstream. To emulate the network and bitstream, create a dlhdl.Simulator object. Use the dlhdl.Simulator object to:

Deploy a custom pretrained series network that detects the presence of speech commands in audio to a Xilinx™ Zynq® UltraScale+™ MPSoC ZCU102 Evaluation Kit. This example uses the pretrained network that was trained by using the Speech Commands Dataset [1]. To create the pretrained network, see Speech Command Recognition Using Deep Learning.

Deploy a pretrained convolutional neural network (CNN) for modulation classification to the Xilinx™ Zynq® UltraScale+™ MPSoC ZCU102 Evaluation Kit. The pretrained network is trained by using generated synthetic, channel-impaired waveforms. To train the trainedNet network, see Modulation Classification with Deep Learning.

Create, compile, and deploy a dlhdl.Workflow object by using the Deep Learning HDL Toolbox™ Support Package for Xilinx FPGA and SoC. The Workflow object has a custom trained human pose estimation network as the network object. The network detects and outputs poses of people present in an input image of size 256-by-192. To train the network, see Estimate Body Pose Using Deep Learning.

Deploy a trained you only look once (YOLO) v3 object detector to a target FPGA board. You then use MATLAB to retrieve the object classification from the FPGA board.

Deploy a trained semantic segmentation network that uses dilated convolutions to a Xilinx® Zynq® Ultrascale+™ ZCU102 SoC development kit. Semantic segmentation networks like DeepLab [1] make extensive use of dilated convolutions, also known as atrous convolutions because they can increase the receptive field of the layer without increasing the number of parameters or computations.

Create a high-resolution image from a low-resolution image using a very-deep super-resolution (VDSR) neural network running on an FPGA. Use Deep Learning HDL Toolbox™ to deploy the VDSR network and MATLAB® to retrieve the high-resolution image. To create the trained VDSR network used in this example, see Increase Image Resolution Using Deep Learning.