XNNPACK is a highly optimized library of floating-point neural network inference operators for ARM, WebAssembly, and x86 (SSE2 level) platforms. XNNPACK is not intended for direct use by deep learning practitioners researchers; instead it provides low-level performance primitives for accelerating high-level machine learning frameworks, such as MediaPipe, TensorFlow Lite, and TensorFlow.js.
- ARM64 on Android and Linux
- ARM on Android
- WebAssembly MVP
- WebAssembly SIMD (experimental)
- x86 and x86-64 (up to SSE2 only) on Android and Linux
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
- Add (tensors of same shape)
- Global Average Pooling
- Channel Shuffle
- Fully Connected
- Clamp (includes ReLU and ReLU6)
All operators in XNNPACK support NHWC layout, but additionally allow custom stride along the Channel 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.
- Marat Dukhan "The Indirect Convolution Algorithm". Presented on Efficient Deep Learning for Compute Vision (ECV) 2019 workshop (slides, paper on ArXiv).
XNNPACK is a based on QNNPACK library. However, unlike QNNPACK, XNNPACK focuses entirely on floating-point operators, and its API is no longer compatible with QNNPACK.