Performance differences from TFLite delegate and Apple CoreML API
Hi @TimYao18, thanks for the data -- it is likely that they are able to optimize better due to internal knowledge of iOS, we would also like to understand this.

@yishuangP can you please take a look? Thanks. @TimYao18 FYR. 

based on my previous experiences of using Core ML and Metal delegates on iOS devices (https://github.com/mlcommons/mobile_app_open):
1. Core ML uses relatively old version of Core ML and ops supported by it are quite limited
2. Metal delegate doesn't support all the ops either

With that, you are likely to run into ops not supported by them. That is, mostly not the ops are accelerated by either ANE or GPU. Hi @TimYao18,

I'm wondering what performance you would get with [AI-Edge-Torch](https://github.com/google-ai-edge/ai-edge-torch)?, you can find more information here: [googleblog](https://developers.googleblog.com/en/ai-edge-torch-high-performance-inference-of-pytorch-models-on-mobile-devices/).

If you want to, you can actually try visualizing the result in [model-explorer](https://github.com/google-ai-edge/model-explorer) as well.

Please try them out and let us know the result. If you still need further help, feel free to open a new issue at a relevant repo.
The question is about the performance difference between TensorFlow Lite Delegate and the Apple Core ML API. 
I test the same model that convert into TFLite and Core ML mlpackage. If the same model is a Core ML model and using Apple's Core ML API, it can be several times faster than using a TFLite model with TensorFlow Lite Delegate. I'd like to understand the differences and the reasons behind them.
![Screenshot 2024-02-02 at 2 38 27 PM](https://github.com/tensorflow/tensorflow/assets/15173100/b2103e7c-984a-423b-84da-81438479cb30)


### 1. System information

- iPhone 15 Pro MAX: iOS 17.3
- TensorFlow library: TensorFlowLiteSwift 0.0.1 nightly

### 2. Code

Please use TFLite's iOS benchmark tool
[https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark/ios](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark/ios)
