Remote Neural Network Conversion for Diverse Client Hardware
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Solution Overview
Problem
Distributing neural network models to diverse client devices with varying operating systems and hardware is inefficient, leading to resource waste and computational overhead due to the need for custom models and limited memory on client devices.
Innovation Solution
Implementing a remote converter system that disassembles and reassembles neural network models based on client device resources, converting lightweight models into larger formats on demand for immediate use, reducing the need for multiple model versions and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple custom neural network models are created for different client device types, then model execution accuracy is improved, but device complexity and resource management difficulty increase
Solution Approach 1:
The patent implements a universal neural network model that can be executed across multiple client device types (iOS, Android, Windows, macOS) without requiring custom versions. The model is designed with platform-agnostic architecture and data formats, allowing a single model to serve multiple functions across different operating systems and hardware configurations, thereby eliminating the need for maintaining multiple model versions.
Solution Approach 2:
The patent segments the neural network model into computationally intensive operations that can be offloaded to remote servers and lighter operations that remain on client devices. This segmentation allows the model to achieve high accuracy through server-side processing while maintaining simplicity on resource-constrained client devices, resolving the contradiction between execution accuracy and device complexity.
2Adaptability or versatility
If all model versions are sent to all client devices, then model availability is improved, but network bandwidth consumption and memory usage increase
Solution Approach 1:
By creating a single universal model that can execute on all platforms, the patent eliminates the need to distribute multiple model versions across the network. This universal approach ensures model availability on all client devices while dramatically reducing network bandwidth consumption and client device memory requirements, as only one model needs to be stored and transmitted regardless of the number of platforms.
Solution Approach 2:
The patent introduces remote servers as intermediaries that host and manage the universal neural network model. These servers act as mediators between the model and diverse client devices, providing model execution capabilities to clients without requiring each client to store or download multiple model versions. This intermediary approach maintains model availability while minimizing network and memory resource consumption on client devices.
3Loss of energy
If lightweight models are used on client devices, then resource consumption is reduced, but model functionality and performance decrease
Solution Approach 1:
The patent segments the neural network model's computational workload between client devices and remote servers. Resource-intensive operations such as complex matrix multiplications and parameter updates are offloaded to servers with abundant computational resources, while lighter operations remain on client devices. This segmentation allows the system to achieve full model functionality and performance while keeping client device resource consumption minimal.
Solution Approach 2:
Remote servers serve as intermediaries that provide enhanced computational capabilities to client devices. These intermediary servers handle the heavy lifting of neural network execution, enabling full model functionality and performance without requiring client devices to possess equivalent computational power. This intermediary approach resolves the contradiction by allowing lightweight client devices to access high-performance model capabilities through server mediation.
Data Source
AI summary
Remote distribution of multiple neural network models to various client devices over a network can be implemented by identifying a native neural network and remotely converting the native neural network to a target neural network based on a given client device operating environment. The native neural network can be configured for execution using efficient parameters, and the target neural network can use less efficient but more precise parameters.