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TensorFlow Lite Core ML delegate enables faster inference on iPhones and iPads

↗ Quelle (blog.tensorflow.org)
🗣️ Stimme:
📑 Inhaltsübersicht
Posted by Tei Jeong and Karim Nosseir, Software Engineers
, delegate. Previously, with Apple's mobile devices — iPhones and iPads — the only option was the GPU delegate.

When Apple released its machine learning framework (NPUs), similar to Google’s that uses Apple's Core ML API to run floating-point models faster on iPhones and iPads with the Neural Engine. We are able to see performance gains up to 14x (see details below) for models like MobileNet and Inception V3.
or later (for example, iPhone XS). For older iPhones, you should use the .

Impacts on performance

We tested the delegate with two common float models, MobileNet V2 and Inception V3. Benchmarks were conducted on the iPhone 8+ (A11 SoC), iPhone XS (A12 SoC) and iPhone 11 Pro (A13 SoC), and tested for three delegate options: CPU only (no delegate), GPU, and Core ML delegate. As mentioned before, you can see the accelerated performance on models with A12 SoC and later, but on iPhone 8+ — where Neural Engine is not available for third parties — there is no observed performance gain when using the Core ML delegate with small models. For larger models, performance is similar to GPU delegate.

In addition to model inference latency, we also measured startup latency. Note that accelerated speed comes at a tradeoff with delayed startup. For the Core ML delegate, startup latency increases along with the model size. For example, on smaller models like MobileNet, we observed a startup latency of 200-400ms. On the other hand, for larger models, like Inception V3, the startup latency could be 2-4 seconds. We are working on reducing the startup latency. The delegate also has an impact on the binary size. Using the Core ML delegate may increase the binary size by up to 1MB.

Models


  • MobileNet V2 (1.0, 224, float) [] : Image Classification
    • Large model. Entire graph runs on Core ML.

Devices

  • iPhone 8+ (Apple A11, iOS 13.2.3)
  • iPhone XS (Apple A12, iOS 13.2.3)
  • iPhone 11 Pro (Apple A13, iOS 13.2.3)

Latencies and speed-ups observed for MobileNet V2

Figure 3: Latencies and speed-ups observed for Inception V3. All versions use a floating-point model. CPU Baseline denotes two-threaded TensorFlow Lite kernels.
* GPU: Core ML uses CPU and GPU for inference. NPU: Core ML uses CPU and GPU, and NPU(Neural Engine) for inference.

How do I use it?

You simply have to make a call on the TensorFlow Lite Interpreter with an instance of the new delegate. For a detailed explanation, please read the is on the roadmap. This will allow acceleration of models with about half the model size and small accuracy loss.
Support for directly or on Twitter with hashtags #TFLite and #PoweredByTF. For bugs or issues, please reach out to us on GitHub.

Acknowledgements

Thank you to Tei Jeong, Karim Nosseir, Sachin Joglekar, Jared Duke, Wei Wei, Khanh LeViet.
Note: Core ML, Neural Engine and Bionic SoCs (A12, A13) are products of Apple Inc.
Vollständiger Original-Bericht
Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org.
↗ Original-Artikel auf blog.tensorflow.org lesen
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