Neural Network Structure Proposal Device for Edge Hardware Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing neural network structure determination methods prioritize recognition accuracy over execution speed, leading to suboptimal utilization of hardware devices, particularly in edge devices, where slight modifications can significantly impact performance, making it difficult to achieve high operational efficiency.
Innovation Solution
A neural network structure proposal device and method that analyzes operational efficiency for each layer of a neural network, estimating execution time and replacing layers with high execution time and low operational efficiency with alternative layers to optimize the structure for the target device, using operational efficiency analysis and layer structure replacement techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experts determine neural network structure with priority on recognition accuracy, then recognition accuracy is improved, but execution speed and hardware utilization deteriorate
Solution Approach 1:
The system automatically adjusts neural network structure parameters (layer types, numbers of layers, numbers of filters, kernel sizes) to optimize execution speed while maintaining recognition accuracy. This resolves the contradiction by shifting from manual expert tuning that prioritizes accuracy to automated parameter optimization that balances both accuracy and speed based on hardware characteristics.
Solution Approach 2:
The neural network structure determination device performs self-optimization of the network structure without requiring expert intervention. The system automatically analyzes hardware characteristics and configures optimal network parameters, enabling the system to serve itself in optimizing the trade-off between recognition accuracy and execution speed.
2Measurement precision
If neural network structure is optimized for high recognition accuracy, then recognition accuracy is improved, but operational efficiency and hardware performance utilization deteriorate
Solution Approach 1:
The system dynamically changes structural parameters of the neural network based on hardware operational efficiency requirements. It automatically adjusts parameters such as the number of layers, filters, and kernel sizes to maximize operational efficiency (GOPS/GFLOPS) while maintaining acceptable recognition accuracy, thereby resolving the contradiction between accuracy and productivity.
Solution Approach 2:
The system incorporates feedback mechanisms that evaluate operational efficiency metrics and use this information to iteratively optimize the neural network structure. By continuously monitoring and adjusting based on hardware performance feedback, the system achieves high operational efficiency without significantly compromising recognition accuracy.
3Device complexity
If device characteristics are not considered in neural network structure determination, then structure configuration is simplified, but execution speed and hardware utilization deteriorate
Solution Approach 1:
The system performs preliminary analysis of hardware device characteristics before determining the neural network structure. By pre-acquiring information about device capabilities, memory, and computational efficiency, the system can automatically configure optimal structures without requiring complex manual tuning, thus resolving the contradiction between configuration simplicity and execution speed.
Solution Approach 2:
The neural network structure determination device acts as an intermediary between hardware characteristics and network configuration. It translates hardware specifications into optimal network parameters automatically, eliminating the need for experts to manually bridge this gap while achieving high execution speed and hardware utilization.
4Adaptability or versatility
If neural network structure is not optimized for specific device characteristics, then versatility is improved, but operational efficiency and processing performance deteriorate
Solution Approach 1:
The system automatically adapts neural network parameters based on specific device characteristics. By dynamically adjusting structure parameters according to each device's capabilities, the system achieves both versatility across different devices and high operational efficiency on each specific device, resolving the contradiction between adaptability and productivity.
Data Source
AI summary
The neural network structure proposal device facilitates finding a neural network structure suitable for a device includes an operational efficiency analysis unit which calculate, for each of a plurality of layers of a neural network having different parameters, an estimated amount of execution time of the layer and operational efficiency corresponding to the computation amount per unit time on a target device, and a layer structure replacing unit which replaces the layer with the large estimated amount of execution time and low operational efficiency with another layer, and outputs neural network structure information indicating structure of the neural network.