uxlfoundation/oneDNN

Health v2 · maintenance only

Adoption not part of the score

4042 stars · 1186 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

oneDNN is an open-source cross-platform performance library providing optimized building blocks (primitives) for deep learning applications on CPUs and GPUs. It implements the oneAPI specification and is optimized for Intel 64/AMD64 processors, Arm AArch64 processors, and Intel Graphics, with experimental support for NVIDIA/AMD GPUs, PPC64, s390x, and RISC-V.

Use cases

• speed up convolution and matmul primitives in my deep learning framework
• optimize inference performance on Intel CPUs and GPUs
• integrate accelerated neural network kernels into a C++ application
• leverage AVX-512, AMX, and bfloat16 hardware features for DNN workloads
• accelerate PyTorch or TensorFlow execution on Intel architecture
• build a custom inference engine with optimized low-level primitives

When to choose

• you are developing a deep learning framework or inference engine and need highly optimized CPU/GPU primitives
• you target Intel hardware (including AMX, AVX-512, Intel Graphics) or Arm AArch64 and want vendor-tuned kernels
• you need a oneAPI-spec-compliant DNN library with SYCL support
• you maintain software like llama.cpp, ONNX Runtime, or OpenVINO-style stacks needing a fast backend

When to avoid

• you are a practitioner who just wants to train or run models - use PyTorch, TensorFlow, or another oneDNN-enabled framework instead
• you need high-level model APIs, automatic differentiation, or training loops - oneDNN provides low-level primitives only
• your primary targets are NVIDIA/AMD GPUs or RISC-V, where support is still experimental
• you need a Python-first library rather than a C/C++ performance library

Facets

library · maturity stable

deep-learning machine-learning gpu-computing benchmarking deep-learning machine-learning gpu-computing performance cpp windows cross-platform onednn oneapi sycl openmp tbb avx512 amx aarch64 bfloat16 neural-network-primitives cpu-optimization linux macos gpu

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markdown · JSON · MCP: product_card(name="uxlfoundation/oneDNN")