Maratyszcza/NNPACK

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1710 stars · 321 forks observed · 2026-08-28

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

NNPACK is a C99 acceleration package providing high-performance SIMD and multi-core CPU implementations of neural network layers, especially convolutional layers, using FFT, Winograd, and implicit GEMM algorithms. It serves as a low-level performance primitive library consumed by deep learning frameworks like PyTorch, Caffe2, and MXNet rather than being used directly by researchers.

Use cases

• speed up convolutional layer inference on multi-core CPUs
• accelerate convnet forward and backward propagation with SIMD
• use FFT-based convolution for large kernels
• use Winograd transform for fast 3x3 convolutions
• integrate optimized NN primitives into a deep learning framework
• run neural network inference on ARM mobile devices with NEON

When to choose

• you need maximum CPU performance for convnet layers in a framework or runtime
• you target x86-64 with AVX2 or ARM with NEON, including mobile and WebAssembly
• you want dependency-free C99 primitives for convolution and fully-connected layers

When to avoid

• you want a high-level ML framework or training toolkit rather than low-level primitives
• you need GPU acceleration
• you need actively developed features or modern CPU instruction sets beyond AVX2, as the project is in maintenance mode

Facets

library · maturity maintenance

machine-learning llm-inference benchmarking concurrency deep-learning machine-learning performance gpu-computing wasm cpp python simd neural-networks convolution cpu-optimization winograd-transform fft inference-primitives c99 linux macos android ios

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