The power consumption of DLA on orin is much higher than that of GPU?

  • Device: nvidia jetson agx orin

  • CNN: Yolov5’s 0-343 layers (all layers support DLA)

  • Method of calculating power consumption: Use the Jetson Power GUI to collect Power Monitor logs on the graphical interface, interval=1s,total power consumption=VDD_ GPU_ SOC, VDD_ CPU_ CV, VIN_ SYS_ 5V0

  • Collection process:

  1. Run yolov5_int8_gpu.engine 1000 times using trtexec, collecting the total power consumption of yolov5 on the GPU (trtexec – loadengine=yolov5_int8_gpu. engine – iterations=1000)

  2. Run yolov5_int8_dla.engine 1000 times using trtexec, collecting the total power consumption of yolov5 on the DLA (trtexec – loadengine=yolov5_int8_dla. engine – iterations=1000)

Although the inst power (VDD_GPU_SOC) of the GPU is about 30000mW when running on the GPU, and the inst power (VDD_CPU_CV) of the DLA and CPU is only about 2800mW when running on the DLA, the inference duration of the DLA is much longer than that of the GPU, resulting in a high total power consumption of the DLA.

The final result is to perform 1000 yolov5 inference tests, with a total power consumption of 210027mJ on the GPU and 582913mJ on the DLA, indicating that the DLA does not save energy.

May I ask if there are any errors in my calculation process? If there are no errors in the calculation, why is DLA not as energy-efficient as the official explanation? The accuracy of the model I am using is int8 instead of fp16.

Thanks in advance.
Best regard,


Does your model can run solely on the DLA?
Or does it have some fallback layer that will run on GPU?



Below is a useful link for your reference.
In our experiment, DLA is about 3~5x power efficient compared to the GPU.


Thanks for your prompt reply.
The CNN model I am using is the 0-343 layers of YOLOv5. I can confirm that its converted engine can run completely on DLA and will not fallback to GPU. The following is the result of visualizing yolov5_int8_dla. engine using TensorRT tools.

This is very useful for me. I will carefully read and learn the content of this link. Thank you again for your help!

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