google/XNNPACK

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2435 stars · 547 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

XNNPACK is a highly optimized C library of floating-point neural network inference operators for ARM, x86, WebAssembly, and RISC-V platforms. It serves as a low-level performance primitive layer used by high-level frameworks like TensorFlow Lite, PyTorch, ONNX Runtime, and MediaPipe rather than being used directly by practitioners.

Use cases

• accelerate neural network inference on mobile devices
• optimize convolution and matrix multiplication kernels for ARM and x86
• run neural network operators in the browser via WebAssembly SIMD
• speed up TensorFlow Lite or PyTorch mobile inference
• implement efficient SIMD-optimized deep learning primitives
• benchmark single-threaded inference performance on phones

When to choose

• you are building or optimizing an ML inference framework and need fast low-level kernels
• you need cross-architecture support including ARM, x86, RISC-V, and WebAssembly
• you need fine-grained control over operator performance, threading, and memory layouts like NHWC with channel slicing

When to avoid

• you are a deep learning practitioner looking for a high-level training or inference API
• you need GPU acceleration rather than CPU-optimized operators
• you want a ready-to-use end-user application rather than a library of primitives

Facets

library · maturity active

machine-learning llm-inference benchmarking concurrency machine-learning deep-learning mobile-development web-development performance embedded-systems windows wasm cross-platform cpp neural-network-inference simd-optimization kernel-library arm-neon risc-v inference-operators low-level-primitives android ios macos linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="google/XNNPACK")