Dynamic Neural Network Efficiency Adjustment via Sub-Network Switching
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Solution Overview
Problem
Existing methods for controlling the status of edge devices, such as temperature, through neural network efficiency adjustments are limited in design flexibility and user convenience.
Innovation Solution
A system comprising a detector and a signal generator that dynamically adjusts the neural network efficiency of a dynamic neural network by detecting changes in the device's status and generating control signals to switch between multiple sub-networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If QoS is utilized to control the status through adjusting hardware setting of the edge device, then the device status can be controlled, but the design flexibility is limited and user convenience is reduced
Solution Approach 1:
The patent implements dynamic adjustment of neural network efficiency by switching between multiple sub-networks with different computational complexities based on real-time device status detection. This allows the system to adapt dynamically to changing thermal conditions rather than using static hardware settings, thereby improving design flexibility while maintaining ease of operation through automated control.
Solution Approach 2:
The system changes the efficiency parameter of the neural network by selecting different sub-networks with varying computational requirements. This parameter change approach allows flexible adaptation to device status without requiring physical hardware modifications, resolving the contradiction between design flexibility and operational simplicity.
2Adaptability or versatility
If neural network efficiency is dynamically adjusted based on device status, then design flexibility is improved, but system complexity increases
Solution Approach 1:
The neural network is segmented into multiple sub-networks with different efficiency levels that can be selectively activated. This segmentation allows the system to achieve design flexibility through software-based switching rather than complex hardware modifications, thereby improving adaptability while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces a detector and signal generator as intermediary components that automatically monitor device status and trigger appropriate sub-network selection. This intermediary mechanism simplifies the control process by automating the decision-making logic, reducing the perceived system complexity while maintaining high design flexibility.
3Temperature
If conventional QoS methods are used to control device status, then temperature control is achieved, but neural network efficiency cannot be dynamically adjusted
Solution Approach 1:
The system dynamically adjusts neural network efficiency in real-time based on detected device status changes, creating a responsive control mechanism that simultaneously manages temperature and maintains productivity. Unlike static QoS methods, this dynamic approach allows continuous optimization of both thermal conditions and computational performance.
Solution Approach 2:
The patent implements a feedback loop where the detector continuously monitors device status and triggers signal generation that adjusts neural network efficiency accordingly. This closed-loop feedback mechanism ensures that temperature control and productivity optimization are achieved through continuous adaptation rather than fixed settings.
Data Source
AI summary
A system for dynamically adjusting neural network efficiency of a dynamic neural network running on a device includes a detector and a signal generator. The detector is arranged to detect a change of a status of the device, to generate a trigger signal. The signal generator is arranged to generate a control signal according to the trigger signal, to dynamically adjust the neural network efficiency of the dynamic neural network.