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, Linux, and macOS

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)
• HardSwish
• PReLU

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.