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-# XNNPACK
-
-XNNPACK is a highly optimized solution for neural network inference on ARM, x86, WebAssembly, and RISC-V platforms. XNNPACK is not intended for direct use by deep learning practitioners and researchers; instead it provides low-level performance primitives for accelerating high-level machine learning frameworks, such as [TensorFlow Lite](https://www.tensorflow.org/lite), [TensorFlow.js](https://www.tensorflow.org/js), [PyTorch](https://pytorch.org/), [ONNX Runtime](https://onnxruntime.ai), and [MediaPipe](https://mediapipe.dev).
-
-## Supported Architectures
-
-- ARM64 on Android, iOS, macOS, Linux, and Windows
-- ARMv7 (with NEON) on Android
-- ARMv6 (with VFPv2) on Linux
-- x86 and x86-64 (up to AVX512) on Windows, Linux, macOS, Android, and iOS simulator
-- WebAssembly MVP
-- WebAssembly SIMD
-- [WebAssembly Relaxed SIMD](https://github.com/WebAssembly/relaxed-simd) (experimental)
-- RISC-V (RV32GC and RV64GC)
-
-## Operator Coverage
-
-XNNPACK implements the following neural network operators:
-
-- 2D Convolution (including grouped and depthwise)
-- 2D Deconvolution (AKA Transposed Convolution)
-- 2D Average Pooling
-- 2D Max Pooling
-- 2D ArgMax Pooling (Max Pooling + indices)
-- 2D Unpooling
-- 2D Bilinear Resize
-- 2D Depth-to-Space (AKA Pixel Shuffle)
-- Add (including broadcasting, two inputs only)
-- Subtract (including broadcasting)
-- Divide (including broadcasting)
-- Maximum (including broadcasting)
-- Minimum (including broadcasting)
-- Multiply (including broadcasting)
-- Squared Difference (including broadcasting)
-- Global Average Pooling
-- Channel Shuffle
-- Fully Connected
-- Abs (absolute value)
-- Bankers' Rounding (rounding to nearest, ties to even)
-- Ceiling (rounding to integer above)
-- Clamp (includes ReLU and ReLU6)
-- Convert (includes fixed-point and half-precision quantization and
-  dequantization)
-- Copy
-- ELU
-- Floor (rounding to integer below)
-- HardSwish
-- Leaky ReLU
-- Negate
-- Sigmoid
-- Softmax
-- Square
-- Tanh
-- Transpose
-- Truncation (rounding to integer towards zero)
-- PReLU
-
-All operators in XNNPACK support NHWC layout, but additionally allow custom stride along the **C**hannel dimension. Thus, operators can consume a subset of channels in the input tensor, and produce a subset of channels in the output tensor, providing a zero-cost Channel Split and Channel Concatenation operations.
-
-## Performance
-
-### Mobile phones
-
-The table below presents **single-threaded** performance of XNNPACK library on three generations of MobileNet models and three generations of Pixel phones.
-
-| Model                   | Pixel, ms | Pixel 2, ms | Pixel 3a, ms |
-| ----------------------- | :-------: | :---------: | :----------: |
-| FP32 MobileNet v1 1.0X  |    82     |      86     |      88      |
-| FP32 MobileNet v2 1.0X  |    49     |      53     |      55      |
-| FP32 MobileNet v3 Large |    39     |      42     |      44      |
-| FP32 MobileNet v3 Small |    12     |      14     |      14      |
-
-The following table presents **multi-threaded** (using as many threads as there are big cores) performance of XNNPACK library on three generations of MobileNet models and three generations of Pixel phones.
-
-| Model                   | Pixel, ms | Pixel 2, ms | Pixel 3a, ms |
-| ----------------------- | :-------: | :---------: | :----------: |
-| FP32 MobileNet v1 1.0X  |    43     |      27     |      46      |
-| FP32 MobileNet v2 1.0X  |    26     |      18     |      28      |
-| FP32 MobileNet v3 Large |    22     |      16     |      24      |
-| FP32 MobileNet v3 Small |     7     |       6     |       8      |
-
-Benchmarked on March 27, 2020 with `end2end_bench --benchmark_min_time=5` on an Android/ARM64 build with Android NDK r21 (`bazel build -c opt --config android_arm64 :end2end_bench`) and neural network models with randomized weights and inputs.
-
-### Raspberry Pi
-
-The table below presents **multi-threaded** performance of XNNPACK library on three generations of MobileNet models and three generations of Raspberry Pi boards.
-
-| Model                   | RPi Zero W (BCM2835), ms | RPi 2 (BCM2836), ms | RPi 3+ (BCM2837B0), ms | RPi 4 (BCM2711), ms | RPi 4 (BCM2711, ARM64), ms |
-| ----------------------- | :----------------------: | :-----------------: | :--------------------: | :-----------------: | :------------------------: |
-| FP32 MobileNet v1 1.0X  |          3919            |         302         |          114           |          72         |             77             |
-| FP32 MobileNet v2 1.0X  |          1987            |         191         |           79           |          41         |             46             |
-| FP32 MobileNet v3 Large |          1658            |         161         |           67           |          38         |             40             |
-| FP32 MobileNet v3 Small |           474            |          50         |           22           |          13         |             15             |
-| INT8 MobileNet v1 1.0X  |          2589            |         128         |           46           |          29         |             24             |
-| INT8 MobileNet v2 1.0X  |          1495            |          82         |           30           |          20         |             17             |
-
-Benchmarked on Feb 8, 2022 with `end2end-bench --benchmark_min_time=5` on a Raspbian Buster build with CMake (`./scripts/build-local.sh`) and neural network models with randomized weights and inputs. INT8 inference was evaluated on per-channel quantization schema.
-
-## Minimum build requirements
-
-- C11
-- C++14
-- Python 3
-
-## Publications
-
-- Marat Dukhan "The Indirect Convolution Algorithm". Presented on [Efficient Deep Learning for Compute Vision (ECV) 2019](https://sites.google.com/corp/view/ecv2019/) workshop ([slides](https://drive.google.com/file/d/1ZayB3By5ZxxQIRtN7UDq_JvPg1IYd3Ac/view), [paper on ArXiv](https://arxiv.org/abs/1907.02129)).
-- Erich Elsen, Marat Dukhan, Trevor Gale, Karen Simonyan "Fast Sparse ConvNets".
-  [Paper on ArXiv](https://arxiv.org/abs/1911.09723), [pre-trained sparse
-  models](https://github.com/google-research/google-research/tree/master/fastconvnets).
-- Marat Dukhan, Artsiom Ablavatski "The Two-Pass Softmax Algorithm".
-  [Paper on ArXiv](https://arxiv.org/abs/2001.04438).
-- Yury Pisarchyk, Juhyun Lee "Efficient Memory Management for Deep Neural Net Inference".
-  [Paper on ArXiv](https://arxiv.org/abs/2001.03288).
-
-## Ecosystem
-
-### Machine Learning Frameworks
-
-- [TensorFlow Lite](https://blog.tensorflow.org/2020/07/accelerating-tensorflow-lite-xnnpack-integration.html).
-- [TensorFlow.js WebAssembly backend](https://blog.tensorflow.org/2020/03/introducing-webassembly-backend-for-tensorflow-js.html).
-- [PyTorch Mobile](https://pytorch.org/mobile).
-- [ONNX Runtime Mobile](https://onnxruntime.ai/docs/execution-providers/Xnnpack-ExecutionProvider.html)
-- [MediaPipe for the Web](https://developers.googleblog.com/2020/01/mediapipe-on-web.html).
-- [Alibaba HALO (Heterogeneity-Aware Lowering and Optimization)](https://github.com/alibaba/heterogeneity-aware-lowering-and-optimization)
-- [Samsung ONE (On-device Neural Engine)](https://github.com/Samsung/ONE)
-
-## Acknowledgements
-
-XNNPACK is a based on [QNNPACK](https://github.com/pytorch/QNNPACK) library. Over time its codebase diverged a lot, and XNNPACK API is no longer compatible with QNNPACK.