cuDNN

E209958

GPU-accelerated library deep learning library

cuDNN is NVIDIA’s GPU-accelerated library of optimized primitives for deep neural networks, widely used to speed up training and inference in frameworks like TensorFlow and PyTorch.

Try in SPARQL Jump to: Surface forms Disambiguation Statements Elicitation Referenced by

All labels observed (3)

Label	Occurrences
cuDNN canonical	7
NVIDIA cuDNN	3
CUDA Deep Neural Network library	2

How this entity was disambiguated

This entity first appeared as the object of triple T1893378 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: cuDNN
Context triple: [NVIDIA CUDA, includes, cuDNN]

A. NVIDIA CUDA
NVIDIA CUDA is a parallel computing platform and programming model that enables developers to use NVIDIA GPUs for general-purpose high-performance computing.
B. CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
C. PlaidML
PlaidML is an open-source, hardware-agnostic deep learning engine designed to accelerate neural network computation on a wide range of GPUs and other devices.
D. NVIDIA AI Enterprise software suite
NVIDIA AI Enterprise software suite is a comprehensive, enterprise-grade collection of AI tools, frameworks, and optimized software designed to accelerate the development and deployment of AI and data analytics workloads across modern data centers and clouds.
E. Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit (CNTK) is an open-source deep learning framework developed by Microsoft for building, training, and deploying neural networks at scale.
F. None of above. chosen
G. Unsure - the case is ambiguous/there is not enough information to decide.

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: cuDNN
Target entity description: cuDNN is NVIDIA’s GPU-accelerated library of optimized primitives for deep neural networks, widely used to speed up training and inference in frameworks like TensorFlow and PyTorch.

A. NVIDIA CUDA
NVIDIA CUDA is a parallel computing platform and programming model that enables developers to use NVIDIA GPUs for general-purpose high-performance computing.
B. CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
C. PlaidML
PlaidML is an open-source, hardware-agnostic deep learning engine designed to accelerate neural network computation on a wide range of GPUs and other devices.
D. NVIDIA AI Enterprise software suite
NVIDIA AI Enterprise software suite is a comprehensive, enterprise-grade collection of AI tools, frameworks, and optimized software designed to accelerate the development and deployment of AI and data analytics workloads across modern data centers and clouds.
E. Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit (CNTK) is an open-source deep learning framework developed by Microsoft for building, training, and deploying neural networks at scale.
F. None of above. chosen

Statements (62)

Predicate	Object
instanceOf	GPU-accelerated library ⓘ deep learning library ⓘ
acronymFor	CUDA Deep Neural Network library ⓘ linked to: cuDNN
developer	NVIDIA ⓘ linked to: NVIDIA Corporation
distribution	NVIDIA Developer website ⓘ
domain	deep learning ⓘ neural networks ⓘ
fullName	CUDA Deep Neural Network library ⓘ linked to: cuDNN
goal	accelerate deep neural network inference ⓘ accelerate deep neural network training ⓘ
integratesWith	NVIDIA CUDA ecosystem ⓘ NVIDIA TensorRT (indirectly via shared primitives) ⓘ linked to: NVIDIA TensorRT
languageBinding	C ⓘ C++ ⓘ
license	proprietary ⓘ
optimizedFor	NVIDIA GPU architectures ⓘ high throughput ⓘ low latency inference ⓘ
programmingModel	CUDA ⓘ linked to: NVIDIA CUDA
provides	GPU-accelerated tensor operations ⓘ GRU primitives ⓘ LSTM primitives ⓘ activation functions ⓘ high-performance convolution routines ⓘ normalization operations ⓘ optimized primitives for deep neural networks ⓘ pooling operations ⓘ recurrent neural network routines ⓘ tensor layout transformations ⓘ
requires	CUDA Toolkit ⓘ linked to: NVIDIA CUDA NVIDIA GPU driver ⓘ
supportsDataType	BF16 ⓘ FP16 ⓘ FP32 ⓘ INT8 ⓘ
supportsFeature	multi-GPU scaling via frameworks ⓘ tensor cores acceleration ⓘ
supportsHardware	NVIDIA GPU ⓘ linked to: GPU NVIDIA data center GPU ⓘ linked to: NVIDIA Tesla data center GPUs NVIDIA embedded GPU ⓘ NVIDIA gaming GPU ⓘ
supportsOperation	RNN inference ⓘ RNN training ⓘ backward convolution ⓘ batch normalization ⓘ dropout ⓘ forward convolution ⓘ softmax ⓘ tensor reduction ⓘ
targetUser	deep learning framework developers ⓘ high-performance computing developers ⓘ
useCase	inference of deep neural networks ⓘ training of deep neural networks ⓘ
usedBy	Caffe ⓘ Caffe2 ⓘ Chainer ⓘ MXNet ⓘ Microsoft Cognitive Toolkit ⓘ PaddlePaddle ⓘ PyTorch ⓘ TensorFlow ⓘ Theano ⓘ

How these facts were elicited

Referenced by (12)

Full triples — surface form annotated when it differs from this entity's canonical label.

NVIDIA CUDA → includes → cuDNN ⓘ

NVIDIA DGX → includesSoftware → NVIDIA cuDNN ⓘ

linked to: cuDNN

NVIDIA AI Enterprise → includes → NVIDIA cuDNN ⓘ

subject linked to: NVIDIA AI Enterprise software suite

linked to: cuDNN

Microsoft Cognitive Toolkit → supportsStandard → cuDNN ⓘ

NVIDIA Tesla data center GPUs → supportsEcosystem → NVIDIA cuDNN ⓘ

linked to: cuDNN

NVIDIA Jetson embedded modules → supports → cuDNN ⓘ

cuDNN → fullName → CUDA Deep Neural Network library ⓘ

linked to: cuDNN

cuDNN → acronymFor → CUDA Deep Neural Network library ⓘ

linked to: cuDNN

CUDA → includesLibrary → cuDNN ⓘ

subject linked to: Compute Unified Device Architecture

Tensor Cores → exposedThrough → cuDNN ⓘ

NVIDIA TensorRT → integratesWith → cuDNN ⓘ

Caffe → supportsBackend → cuDNN ⓘ

All labels observed (3)

How this entity was disambiguated Show

Statements (62)

How these facts were elicited Show

Referenced by (12)

How this entity was disambiguated

How these facts were elicited