Hello,
Don’t worry you were asking right question here.
It may not be possible to do apples to apples comparison directly. But there are few avenues where you can compare performance of hardware in Deep learning models. I would like to keep this bench marking simple by just comparing a fixed set of models.


Comparison of int8.tflite models
there are quite few models which are supported on both hardware ( Mobilenet, Unet, Resenet). You can find Coral version of models here ( coral.ai/models ). So you can compare inference time of cpu int8.tflite on Jetson and edgetpu.tflite on Coral.


Comparison of FP16 vs Edgetpu.tflite
You can use same models from above. But compare inference time of FP16 models on Jetson vs Edgetpu.tflite models on Coral.  Since Fp16 models are 2x the size of Int8 models; just to keep the compute work load fair export the coral models with 2x input shape.


I assume your project is limited to inference time comparison only.
Hope this helps,
Good luck