Demo 2 & 3 - MCU Target, pipelines and structure
Estimated time : 5 minutes
Fill the form
Go to http://157.26.64.222/benchmark.
Form 1 :
- Target :
NXP Cup board, MCU LPC55S69JBD100 - Runtime :
TensorFlow Lite - Model :
small_model.tflite
From 2 :
- Target :
Jetson Orin Nano - Runtime :
TensorRT - Model :
big_model.onnx
Click on the Launch the benchmark button.
Key talking points 1 :
- Target : The target this time is an MCU based on a Cortex M33.
- Runtime : The runtime is ThensoFlow Lite for microcontrollers. This benchmark will take more time than the previous one.
- Model : The selected model is very small, this target has very limited memory resources.
Key talking points 2 :
- Target : The target is a Jetson Orin Nano, a powerful device with a GPU.
- Runtime : The runtime is TensorRT, a runtime optimized for NVIDIA GPUs.
- Model : The model is a big model, for embedded standards. It will take more time to run.
Wait for the results & show the pipeline and the target repository
The results should be available in about 3 minutes.
Got to the pipeline page and show the different steps of the benchmark. Key talking points :
- Pipeline : The pipeline is the set of steps that are executed to run the benchmark.
- Runner : The runner is selected by the VirtualLab based on the Runner tags.
- Steps :
- Prepare : The VirtualLav prepares the environment. It downloads the model and the target sources to the runner.
- Run : The VirtualLab runs the different scripts to perform the benchmark.
- Upload : The runner uploads the result to the VirtualLab.
- End : The VirtualLab displays the result to the user.
Go to the targets repository page and show the different repository. Select and show the JetsonOrinNano_TRT.
Here is an MCU repository for the LPC55S69_TFLite.
Key talking points :
- Structure : The structure of the target repository is important. It contains all the scripts and files needed to run the benchmark.
- AI_Support : Prepare the environment and generate the appropriate code for the target if needed.
- AI_Build : Build the target code.
- AI_Deploy : Deploy the model and the target code to the target.
- AI_Manager : Run the benchmark and measure the performance.
- AI_Project : Contains the target project and code base.
Analyze the results
Key talking points :
- Small Model : The model is very small, the inference time is low.
In case of problems LPC55S69JBD100 :
- Here is the resulting JSON.
- Here is the pipeline of the same benchmark.
In case of problems Jetson Orin Nano :
- Here is the resulting JSON.
- Here is the pipeline of the same benchmark.
Compare the results
Go to the history page and compare the two results.
Key talking points :
- Models : The models used and the targets are different. Thus comparing the results is not relevant. But it is here just to illustrate the feature.
