Google adds automatic acceleration and integrates TensorFlow Lite into Android

Google adds automatic acceleration and integrates TensorFlow Lite into Android

Tech Highlights:

  • This will reduce overall device storage usage, since TensorFlow Lite will be shared by all apps. Storage usage can be a significant concern, Google says, for many apps that are size-constrained, even more so given the fact TensorFlow Lite is not exactly a small library. In addition, this will make it possible to automatically update TensorFlow Lite, just as happens with any other Android component installed through Google Play Services. Automatic update is beneficial, according to Google, in that often developers stick with some older versions of TensorFlow Lite in order to maximize API availability.

  • In order to address a variety of issues that developers encounter when utilising on-device machine learning, Google has unveiled the Android ML Platform, a new mobile ML stack based on TensorFlow Lite. The Android ML Platform’s base, TensorFlow Lite, will be made available on all Android devices that support Google Play Services, which is the real announcement behind it. It will consequently become a component of the framework supporting the Android operating system. As a result, Google and third-party apps won’t need to include it in their packages anymore, and developers can take its API’s accessibility for granted.

With the Android ML Platform, though, Google is heading to a much more ambitious goal than simply making TensorFlow Lite available by default. In fact, one of the major issues that it is attempting to tackle is device heterogeneity, which can be a factor of staggering complexity in the Android world. This shows in relation to performance as well as testing. In addition to its basic capabilities, TensorFlow Lite will provide Automatic Acceleration to enable models to automatically leverage hardware acceleration when available on device. This feature, which will become available later this year says Google, is based on the creation of allow-lists for specific devices which take performance, accuracy and stability into account. Allow-lists are created when testing a model and can be used at runtime to decide when to use hardware acceleration. Automatic Acceleration will require developers to provide additional metadata to verify correctness.

As a consequence of supporting TensorFlow Lite automatic update through Google Play Services, Google is also stabilizing the Neural Networks API across Android versions. This will prevent a non-backward compatible update from breaking existing apps. Additionally, to make it easier for developers to test their apps, Google says it is working with chipset vendors to make it possible to reduce testing from thousands of devices to a handful of configurations. The Android ML Platform is still in preview and early access can be requested by interested developers.

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