Purely AI News: For AI professionals in a hurry
July 25, 2020
Google's tensorflow-lite framework for deep learning is now more than 2x faster on average, using operator fusion and optimizations for additional CPU instruction sets
TensorFlow Lite, Google's open-source deep learning framework, is a lightweight solution for mobile and embedded devices. It enables low-latency inference of on-device machine learning models with a small binary size and fast performance supporting hardware acceleration.
In the latest version of the TensorFlow Lite framework, Google Research has introduced several community-requested optimizations to make inference on edge IoT devices and smartphones faster and more efficient. TensorFlow Lite already had support for inference using mobile GPUs, Android’s Neural Network API, Hexagon DSPs, Edge TPUs, and the Apple Neural Engine. Integration with the production-tested XNNPACK library further improves inference performance by a factor of 2x on average.

Leveraging the XNNPACK library, Google has made several optimizations for the CPU instruction sets SSE2, SSE4, AVX, AVX2, and AVX512. Additionally, rather than executing TensorFlow Lite operators one-by-one, XNNPACK looks at the whole computational graph and optimizes it through operator fusion. For example, convolution with explicit padding is represented in TensorFlow Lite via a combination of PAD operator and a CONV_2D operator with VALID padding mode. XNNPACK detects this combination of operators and fuses the two operators into a single convolution operator with explicitly specified padding. The XNNPACK backend boosted background segmentation in Pixel 3a Playground by 5X and delivered 2X speedup on neural network models in Augmented Faces API in ARCore. We found that TensorFlow Lite benefits the most from the XNNPACK backend on small neural network models and low-end mobile phones.

The new TensorFlow Lite version 2.3 includes these optimizations based on the XNNPACK backend. Pre-built TensorFlow Lite binaries for Android and iOS can enable these performance improvements with a one-line code change. XNNPACK backend is also supported in the Windows, macOS, and Linux builds of TensorFlow Lite, where it is enabled via a build-time opt-in mechanism. After extensive testing and feedback from the community, Google plans to enable it by default on all platforms in a future release.
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