Updated: April 18, 2020
The following represents a high level overview of our 2020 plan. You should be aware that this roadmap may change at any time and the order below does not reflect any type of priority. As a matter of principle, we typically prioritize issues based on the number of users affected.
We break our roadmap into four key segments: usability, performance, optimization and portability. We strongly encourage you to comment on our roadmap and provide us feedback in the TF Lite discussion group.
- Expanded ops coverage
- Prioritized op additions based on user feedback
- Improvements to using TensorFlow ops in TensorFlow Lite
- Pre-built libraries available via Bintray (Android) and Cocoapods (iOS)
- Smaller binary size when using select TF ops via op stripping
- LSTM / RNN support
- Full LSTM and RNN conversion support, including support in Keras
- Pre-and-post processing support libraries and codegen tool
- Ready-to-use API building blocks for common ML tasks
- Support more models (e.g. NLP) and more platforms (e.g. iOS)
- Android Studio Integration
- Drag & drop TFLite models into Android Studio to generate model binding classes
- Control Flow & Training on-device
- Support for training on-device, focused on personalization and transfer learning
- Visualization tooling with TensorBoard
- Provide enhanced tooling with TensorBoard
- Model Maker
- Support more tasks, including object detection and BERT-based NLP tasks
- More models and examples
- More examples to demonstrate model usage as well as new features and APIs, covering different platforms.
- Task Library
- Improve the usability of the C++ Task Library, such as providing prebuilt binaries and creating user-friendly workflows for users who want to build from source code.
- Release reference examples of using the Task Library.
- Enable more task types.
- Improve cross-platform support and enable more tasks for iOS.
- Better tooling
- Public dashboard for tracking performance gains with each release
- Improved CPU performance
- New highly optimized floating-point kernel library for convolutional models
- First-class x86 support
- Updated NN API support
- Full support for new Android R NN API features, ops and types
- GPU backend optimizations
- Vulkan support on Android
- Support integer quantized models
- Hexagon DSP backend
- Per-channel quantization support for all models created through post-training quantization
- Dynamic input batch size support
- Better op coverage, including LSTM
- Core ML backend
- Optimizing start-up time
- Dynamic quantized models support
- Float16 quantized models support
- Better op coverage
- Post-training quantization for (8b) fixed-point RNNs
- During-training quantization for (8b) fixed-point RNNs
- Quality and performance improvements for post-training dynamic-range quantization
Pruning / sparsity
Sparse model execution support in TensorFlow Lite - WIP
Weight clustering API
- Microcontroller Support
- Add support for a range of 32-bit MCU architecture use cases for speech and image classification
- Sample code and models for vision and audio data
- Full TF Lite op support on microcontrollers
- Support for more platforms, including CircuitPython support