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What is QuickSet Cloud Device Knowledge Graph?

A global device knowledge graph provides structured and detailed insights into electronic devices, outlining their features, functionalities, and the relationships between them. Each household device carries a variety of attributes, such as its brand, model number, manufacturer details, available features and services, both hardware and software characteristics, compatible devices, geographical information, and much more. This extensive repository of knowledge on nearly every audiovisual gadget worldwide is maintained within QuickSet’s device knowledge graph. Utilizing this graph, QuickSet is able to deliver a broad range of features for each device, going beyond simple control options. Moreover, the knowledge graph adds crucial context to user commands and actions, enabling the real-time detection of nearby devices. The algorithms that QuickSet employs rely on the knowledge graph, which includes devices with various control options, communication methods, and protocols, thereby ensuring smooth interactions between devices. Ultimately, this interconnected framework significantly enhances the user experience by streamlining device management, making it not only easier but also more effective. Additionally, users benefit from the seamless integration of multiple devices, allowing for a more cohesive smart home environment.

What is Luminal?

Luminal is an advanced machine-learning framework that prioritizes performance, ease of use, and modularity, utilizing static graphs and compiler-based optimization techniques to handle intricate neural networks efficiently. By converting models into a streamlined set of minimal "primops," consisting of only 12 essential operations, Luminal can perform compiler passes that replace these with optimized kernels suited for particular devices, enabling high-performance execution on GPUs and other hardware platforms. The framework features modules that act as the core building blocks of networks, complemented by a standardized forward API and the GraphTensor interface, which allows for the definition and execution of typed tensors and graphs during compile time. With a focus on maintaining a small and adaptable core, Luminal promotes extensibility through the incorporation of external compilers that support diverse datatypes, devices, training methodologies, and quantization strategies. To facilitate user adoption, a quick-start guide is provided, helping users to clone the repository, build a straightforward "Hello World" model, or run more complex models such as LLaMA 3 with GPU support, simplifying the process for developers looking to tap into its capabilities. Overall, Luminal's flexible architecture positions it as a formidable resource for both newcomers and seasoned experts in the field of machine learning, bridging the gap between simplicity and advanced functionality.

Media

Media

Integrations Supported

Hugging Face
Llama 3

Integrations Supported

Hugging Face
Llama 3

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

QuickSet Cloud

Company Location

United States

Company Website

quicksetcloud.com/device-knowledge-graph/

Company Facts

Organization Name

Luminal

Company Location

United States

Company Website

luminalai.com

Categories and Features

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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