Performance best practices

Mobile and embedded devices have limited computational resources, so it is important to keep your application resource efficient. We have compiled a list of best practices and strategies that you can use to optimize your model and application when using TensorFlow Lite.

Choose the best model for the task

Depending on the task, you will need to make a tradeoff between model complexity and size. If your task requires high accuracy, then you may need a large and complex model. For tasks that require less precision, it is better to use a smaller model because they not only use less disk space and memory, but they are also generally faster and more energy efficient. For example, graphs below show accuracy and latency tradeoffs for some common image classification models.

Graph of model size vs accuracy

Graph of accuracy vs latency

One example of models optimized for mobile devices are MobileNets, which are optimized for mobile vision applications. Hosted models lists several other models that have been optimized specifically for mobile and embedded devices.

You can retrain the listed models on your own dataset by using transfer learning. Check out our transfer learning tutorial for image classification and object detection.

Profile your model

Once you have selected a candidate model that is right for your task, it is a good practice to profile and benchmark your model. TensorFlow Lite benchmarking tool has a built-in profiler that shows per operator profiling statistics. This can help in understanding performance bottlenecks and which operators dominate the computation time.

Profile and optimize operators in the graph

If a particular operator appears frequently in the model and, based on profiling, you find that the operator consumes the most amount of time, you can look into optimizing that operator. This scenario should be rare as TensorFlow Lite has optimized versions for most operators. However, you may be able to write a faster version of a custom op if you know the constraints in which the operator is executed. Check out our custom operator documentation.

Optimize your model

Model compression aims to create smaller models that are generally faster and more energy efficient, so that they can be deployed on mobile devices.

Quantization

If your model uses floating-point weights or activations, then it may be possible to reduce the size of model up to ~4x by using quantization, which effectively turns the float weights to 8-bit. There are two flavors of quantization: post-training quantization and quantized training. The former does not require model re-training, but, in rare cases, may have accuracy loss. When accuracy loss is beyond acceptable thresholds, quantized training should be used instead.

We strongly recommend running benchmarks to make sure that the accuracy is not impacted during model compression. Check out our model optimization docs for details.

Tweak the number of threads

TensorFlow Lite supports multi-threaded kernels for many operators. You can increase the number of threads and speed up execution of operators. Increasing the number of threads will, however, make your model use more resources and power.

For some applications, latency may be more important than energy efficiency. You can increase the number of threads by setting the number of interpreter threads. Multi-threaded execution, however, comes at the cost of increased performance variability depending on what else is executed concurrently. This is particularly the case for mobile apps. For example, isolated tests may show 2x speed-up vs single-threaded, but, if another app is executing at the same time, it may result in worse performance than single-threaded.

Eliminate redundant copies

If your application is not carefully designed, there can be redundant copies when feeding the input to and reading the output from the model. Make sure to eliminate redundant copies. If you are using higher level APIs, like Java, make sure to carefully check the documentation for performance caveats. For example, the Java API is a lot faster if ByteBuffers are used as inputs.

Profile your application with platform specific tools

Platform specific tools like Android profiler and Instruments provide a wealth of profiling information that can be used to debug your app. Sometimes the performance bug may be not in the model but in parts of application code that interact with the model. Make sure to familiarize yourself with platform specific profiling tools and best practices for your platform.

Evaluate whether your model benefits from using hardware accelerators available on the device

TensorFlow Lite has added new ways to accelerate models with faster hardware like GPUs, DSPs, and neural accelerators. Typically, these accelerators are exposed through delegate submodules that take over parts of the interpreter execution. TensorFlow Lite can use delegates by:

  • Using Android's Neural Networks API. You can utilize these hardware accelerator backends to improve the speed and efficiency of your model. To enable the Neural Networks API, call UseNNAPI on the interpreter instance.
  • A binary-only GPU delegate has been released for Android and iOS, using OpenGL and Metal, respectively. To try them out, see the GPU delegate tutorial and documentation.
  • It is possible to create your own delegate if you have access to non-standard hardware. See TensorFlow Lite delegates for more information.

Be aware that some accelerators work better for different types of models. It is important to benchmark each delegate to see if it is a good choice for your application. For example, if you have a very small model, it may not be worth delegating the model to either the NN API or the GPU. Conversely, accelerators are a great choice for large models that have high arithmetic intensity.

Need more help

The TensorFlow team is happy to help diagnose and address specific performance issues you may be facing. Please file an issue on GitHub with details of the issue.