I have measured the performance number of the individual Vitis-AI samples application such as facedetect, classification, and so on by using the Vitis_AI samples respective performance benchmark application.
example - facedetect
- test_performance_facedetect densebox_640x360
Below are my observation and question with respect to the Vitis-AI sample's performance
1 - How exactly test_performance_facedetect densebox_640x360 is measuring the performance number because of the inference performance number of the sample application "test_video_facedetect densebox_640x360" (running on video file 640x360) almost 2x time low as compare to the number which I am getting from the test_performance_facedetect.
2 - Why the Vitis-AI samples application giving me the same performance number even if I am increasing the DPU core number to two (4096) from one (4096)? It seems the sample application is not utilizing the second core of DPU
3 - Any suggestion to optimize and utilize the DPU cores efficiently?
Thank you in advance.
