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How to run the most optimized car simulation
Yannick Goumaz edited this page Jul 7, 2023
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This page explains step-by-step how to start the most optimized autonomous car controller on jumax-cpu to execute CNN inference on FPGA. Webots is running on jumaxbuild1 to take advantage of the GPU. Both processes communicate through a TCP connection. The detailed results are listed and explained in Deliverable 2: CNN performance comparison CPU/FPGA.
- Connect to jumaxbuild1 through SSH.
- Compile Webots from sources (master branch). Be sure to compile the generic resources (which include the driver library) correctly so that the following steps can be performed without problems.
- Start Webots with GUI. You can also start it in headless mode for better results.
- Open autonomous_car_test.wbt.
- Select the
<extern>controller for the car. This way the controller running remotely on jumax-cpu will be able to connect to the instance of Webots. - In a new terminal, connect to jumax-cpu through SSH.
- To start the extern controller, use the
webots-controllerlauncher. The IP address of jumaxbuild1 is192.168.18.3and the controller connects to the Webots instance through TCP.$WEBOTS_HOME/webots-controller --protocol=tcp --ip-address=192.168.18.3 ~/optima/CNN-autonomous-car/controllers/CNN_autonomous_car_fpga_optimized/CNN_autonomous_car_fpga_optimized - Once the weights are loaded on the FPGA, the simulation should be able to run at a ratio of ~7 in fast mode.
- Basics of DFE Applications
- Start MaxIDE
- Run DFE Applications with MaxIDE
- Compile DFE Applications for Webots
- Compilation Debug and Timing Improvement
- Implementation of multilayers perceptrons on CPU :
- Adapting the multilayers perceptron (MLP) inference for FPGA:
- Implementation of a state-of-the-art robotic application in simulation
- Convolutional Neural Network inference on CPU
- Convolutional Neural Network inference on FPGA
- Deliverable 1: CNN performance comparison CPU/FPGA
- Deliverable 2: CNN performance comparison CPU/FPGA
- How to run the most optimized car simulation