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tensorflow/lite/g3doc/guide/roadmap.md

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+# TensorFlow Lite Roadmap
+
+**Updated: May, 2021**
+
+The following represents a high level overview of our roadmap. You should be
+aware that this roadmap may change at any time and the order below does not
+reflect any type of priority.
+
+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
+[TensorFlow Lite discussion group](https://groups.google.com/a/tensorflow.org/g/tflite).
+
+## Usability
+
+*   **Expanded ops coverage**
+    *   Add targeted ops based on user feedback.
+    *   Add targeted op sets for specific domains and areas including Random
+        ops, base Keras layer ops, hash tables, select training ops.
+*   **More assistive tooling**
+    *   Provide TensorFlow graph annotations and compatibility tools to validate
+        TFLite and hardware accelerator compatibility during-training and
+        after-conversion.
+    *   Allow targeting and optimizing for specific accelerators during
+        conversion.
+*   **On-device training**
+    *   Support on-device training for personalization and transfer learning,
+        including a Colab demonstrating end-to-end usage.
+    *   Support variable/resource types (both for inference and training)
+    *   Support converting and executing graphs with multiple function (or
+        signature) entry-points.
+*   **Enhanced Android Studio integration**
+    *   Drag and drop TFLite models into Android Studio to generate model
+        interfaces.
+    *   Improve Android Studio profiling support, including memory profiling.
+*   **Model Maker**
+    *   Support newer tasks, including object detection, recommendation, and
+        audio classification, covering a wide collection of common usage.
+    *   Support more data sets to make transfer learning easier.
+*   **Task Library**
+    *   Support more model types (e.g. audio, NLP) with associated pre and post
+        processing capabilities.
+    *   Update more reference examples with Task APIs.
+    *   Support out-of-the-box acceleration for all tasks.
+*   **More SOTA models and examples**
+    *   Add more examples (e.g. audio, NLP, structure-data related) to
+        demonstrate model usage as well as new features and APIs, covering
+        different platforms.
+    *   Create shareable backbone models for on-device to reduce training and
+        deployment costs.
+*   **Seamless deployment across multiple platforms**
+    *   Run TensorFlow Lite models on the web.
+*   **Improved cross-platform support**
+    *   Extend and improve APIs for Java on Android, Swift on iOS, Python on
+        RPi.
+    *   Enhance CMake support (e.g., broader accelerator support).
+*   **Better frontend support**
+    *   Improve compatibility with various authoring frontends, including Keras,
+        tf.numpy.
+
+## Performance
+
+*   **Better tooling**
+    *   Public dashboard for tracking performance gains with each release.
+    *   Tooling for better understanding graph compatibility with target
+        accelerators.
+*   **Improved CPU performance**
+    *   XNNPack enabled by default for faster floating point inference.
+    *   End-to-end half precision (float16) support with optimized kernels.
+*   **Updated NN API support**
+    *   Full support for newer Android version NN API features, ops, and types.
+*   **GPU optimizations**
+    *   Improved startup time with delegate serialization support.
+    *   Hardware buffer interop for zero-copy inference.
+    *   Wider availability of on device acceleration.
+    *   Better op coverage.
+
+## Optimization
+
+*   **Quantization**
+
+    *   Selective post-training quantization to exclude certain layers from
+        quantization.
+    *   Quantization debugger to inspect quantization error losses per each
+        layer.
+    *   Applying quantization-aware training on more model coverage e.g.
+        TensorFlow Model Garden.
+    *   Quality and performance improvements for post-training dynamic-range
+        quantization.
+    *   Tensor Compression API to allow compression algorithms such as SVD.
+
+*   **Pruning / sparsity**
+
+    *   Combine configurable training-time (pruning + quantization-aware
+        training) APIs.
+    *   Increase sparity application on TF Model Garden models.
+    *   Sparse model execution support in TensorFlow Lite.
+
+## Portability
+
+*   **Microcontroller Support**
+    *   Add support for a range of 32-bit MCU architecture use cases for speech
+        and image classification.
+    *   Audio Frontend: In-graph audio pre-processing and acceleration support
+    *   Sample code and models for vision and audio data.