Error while running mobilenetV1 on Qualcomm SM8550 device using CLML SDK

Troubleshooting
10

Hi @srkreddy1238

I’ve one more doubt regarding CLML SDK, Is the Deconv or transposed convolutional layer (that is generally used in Image segmentation models like UNet) is supported and optimized ?

I was able to tune, compile and run a variant of U-Net model on SM8550 device, but the transpose convolutional layers take almost 80% of total time.

Also, the time taken when using TFLITE (same above model) is almost 10 times lesser than using TVM+CLML SDK (mostly owing to the transpose conv layers).

Also, I see the post about CLML SDK that does not mention DeConv or transpose Conv in the list of supported layers:

Accelerate your machine learning networks using TVM and the Adreno OpenCL ML APIs on Adreno GPUs - Qualcomm Developer Network.

As a follow-up, incase this layer is not supported, is there any plan to support it in future.

Thanks.

P.S:- Please let me if I’ve to open a new discussion thread for this question, 'coz it’s off-topic for this thread.