MPSCNNKernel
E849983
MPSCNNKernel is a core Metal Performance Shaders class that encapsulates and executes high-performance convolutional neural network operations on Apple GPUs.
All labels observed (4)
| Label | Occurrences |
|---|---|
| MPSCNNKernel canonical | 3 |
| MPSCNNBatchNormalization | 1 |
| MPSCNNConvolution | 1 |
| MPSCNNPooling | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T10214150 — 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: MPSCNNKernel Context triple: [Metal Performance Shaders, supports, MPSCNNKernel]
-
A.
ShuffleNetV2
ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
-
B.
LeNet
LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
-
C.
NPU
NPU is a leading Chinese research university in Xi’an renowned for its strengths in aeronautics, astronautics, and marine engineering.
-
D.
NPU
NPU is the commonly used abbreviation for the National Police of Ukraine, the country’s central law enforcement agency responsible for maintaining public order and safety.
-
E.
MobileNetV2
MobileNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, widely used in computer vision applications and available in libraries like torchvision.
- 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: MPSCNNKernel Target entity description: MPSCNNKernel is a core Metal Performance Shaders class that encapsulates and executes high-performance convolutional neural network operations on Apple GPUs.
-
A.
ShuffleNetV2
ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
-
B.
LeNet
LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
-
C.
NPU
NPU is a leading Chinese research university in Xi’an renowned for its strengths in aeronautics, astronautics, and marine engineering.
-
D.
NPU
NPU is the commonly used abbreviation for the National Police of Ukraine, the country’s central law enforcement agency responsible for maintaining public order and safety.
-
E.
MobileNetV2
MobileNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, widely used in computer vision applications and available in libraries like torchvision.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
Metal Performance Shaders class
ⓘ
Objective-C class ⓘ convolutional neural network kernel abstraction ⓘ |
| availableInLanguage | Swift ⓘ |
| category | neural network kernel ⓘ |
| conformsTo | NSCopying ⓘ |
| developedBy | Apple Inc. ⓘ |
| documentationURL | https://developer.apple.com/documentation/metalperformanceshaders/mpscnnkernel ⓘ |
| executesOn | MTLCommandBuffer ⓘ |
| framework |
MetalPerformanceShaders.framework
ⓘ
linked to:
Metal Performance Shaders
|
| introducedIn |
iOS 10
ⓘ
macOS 10.13 ⓘ |
| method |
encodeBatchToCommandBuffer:sourceImages:destinationImages:
ⓘ
encodeToCommandBuffer:sourceImage:destinationImage: ⓘ encodeToCommandBuffer:sourceImage:destinationState:destinationImage: ⓘ |
| optimizedFor | GPU acceleration ⓘ |
| partOf | Metal Performance Shaders ⓘ |
| programmingLanguage | Objective-C ⓘ |
| property |
clipRect
ⓘ
destinationFeatureChannelOffset ⓘ dilationRateX ⓘ dilationRateY ⓘ edgeMode ⓘ offset ⓘ sourceFeatureChannelOffset ⓘ strideInPixelsX ⓘ strideInPixelsY ⓘ |
| requiresFramework | Metal ⓘ |
| runsOn | Apple GPU ⓘ |
| superclass |
MPSKernel
ⓘ
linked to:
MPS
|
| supportsBatchProcessing | yes ⓘ |
| supportsOperation |
activation
ⓘ
binary convolution ⓘ convolution ⓘ depthwise convolution ⓘ fully connected layer ⓘ normalization ⓘ pooling ⓘ softmax ⓘ |
| supportsPlatform |
iOS
ⓘ
macOS ⓘ tvOS ⓘ |
| threadSafe | no ⓘ |
| usedFor |
convolutional neural network operations
ⓘ
deep learning inference ⓘ image processing ⓘ |
| uses |
MPSImage
ⓘ
MPSState ⓘ
linked to:
MPS
MTLDevice ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: MPSCNNKernel Description of subject: MPSCNNKernel is a core Metal Performance Shaders class that encapsulates and executes high-performance convolutional neural network operations on Apple GPUs.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.