Automatic Neural Network Tuning Framework
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Solution Overview
Problem
Designing a neural network optimized for target hardware is challenging due to the need for in-depth knowledge of application and hardware architecture, making manual processes costly and inefficient.
Innovation Solution
A framework for automatically tuning neural network parameters, including modifications such as pruning, decomposition, precision modification, convolution kernel substitution, activation function substitution, and scaling, to improve computational efficiency while maintaining performance requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual tuning of neural network parameters is performed, then optimization for target hardware can be achieved, but the process becomes costly and time-consuming
Solution Approach 1:
The system performs automatic tuning of neural network parameters without requiring manual intervention. The automated framework analyzes the neural network architecture, identifies optimization opportunities, and applies modifications such as pruning, decomposition, and precision changes to optimize computational efficiency while maintaining performance requirements.
Solution Approach 2:
The system modifies neural network parameters including precision levels, architecture decomposition, and parameter quantization to optimize for target hardware. By systematically changing these parameters and evaluating performance, the system achieves hardware-optimized neural networks without manual tuning.
2Use of energy by moving object
If neural network parameters are modified to improve computational efficiency, then power consumption is reduced, but performance may be compromised
Solution Approach 1:
The automated tuning system incorporates feedback mechanisms to evaluate neural network performance after parameter modifications. By measuring accuracy, precision, and other performance metrics against the modified network, the system verifies that efficiency improvements do not compromise performance requirements and adjusts parameters accordingly.
Solution Approach 2:
The system applies selective modifications to specific portions of the neural network rather than uniformly modifying all parameters. By identifying and optimizing only the critical components that impact both efficiency and performance, the system achieves power reduction while maintaining required performance levels.
Data Source
AI summary
Tuning a neural network may include selecting a portion of a first neural network for modification to increase computational efficiency and generating, using a processor, a second neural network based upon the first neural network by modifying the selected portion of the first neural network while offline.