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gptkbp:instance_of
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gptkb:Accelerator
|
|
gptkbp:aims_to
|
accelerate AI workloads
|
|
gptkbp:architecture
|
open architecture
|
|
gptkbp:available_on
|
open-source
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gptkbp:contributes_to
|
gptkb:AI_technology
|
|
gptkbp:designed_for
|
deep learning applications
|
|
gptkbp:developed_by
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gptkb:NVIDIA
|
|
gptkbp:enables
|
scalable solutions
real-time inference
|
|
gptkbp:enhances
|
machine learning performance
|
|
gptkbp:facilitates
|
edge computing
|
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gptkbp:features
|
hardware acceleration
tensor cores
|
|
https://www.w3.org/2000/01/rdf-schema#label
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NVIDIA Deep Learning Accelerator (NVDLA)
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|
gptkbp:is_compatible_with
|
gptkb:NVIDIA_Xavier
gptkb:NVIDIA_Jetson
various frameworks
NVIDIA Orin
|
|
gptkbp:is_designed_for
|
embedded systems
AI edge devices
|
|
gptkbp:is_documented_in
|
NVDLA documentation
|
|
gptkbp:is_integrated_with
|
gptkb:NVIDIA_So_Cs
|
|
gptkbp:is_optimized_for
|
gptkb:resource_utilization
low latency
high throughput
|
|
gptkbp:is_part_of
|
gptkb:NVIDIA_AI_platform
NVIDIA ecosystem
|
|
gptkbp:is_promoted_by
|
NVIDIA developers
|
|
gptkbp:is_supported_by
|
NVIDIA software tools
|
|
gptkbp:is_targeted_at
|
hardware engineers
system architects
AI developers
|
|
gptkbp:is_used_in
|
gptkb:cloud_computing
data centers
|
|
gptkbp:is_utilized_in
|
gptkb:Telecommunications
gptkb:financial_services
industrial automation
smart cities
healthcare applications
|
|
gptkbp:provides
|
low power consumption
high throughput
high efficiency
flexible design options
|
|
gptkbp:released_in
|
gptkb:2017
|
|
gptkbp:supports
|
multiple data types
AI model training
convolutional neural networks
fully connected layers
activation functions
AI model inference
|
|
gptkbp:used_in
|
gptkb:vehicles
gptkb:robotics
smart cameras
|
|
gptkbp:bfsParent
|
gptkb:NVIDIA
|
|
gptkbp:bfsLayer
|
4
|