Neural Network Activation Output Limit Parameter Adjustment
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Solution Overview
Problem
Neural networks trained with low-precision quantization face accuracy loss due to noise, which diminishes their performance in recognizing patterns, highlighting the need for efficient methods to balance precision reduction with performance maintenance.
Innovation Solution
The system initializes and modifies an output limit for the activation function of a neural network during training, using it as a parameter to determine the output, and employs clipping and regularization to mitigate accuracy degradation, allowing for reduced precision while maintaining performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If low-precision quantization is used in neural networks, then processing efficiency and cost are improved, but accuracy deteriorates due to noise
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the output limit parameter of activation functions during training. The system initializes an output limit parameter and modifies it throughout the training process to optimize both precision and efficiency. This parameter adjustment allows the neural network to adapt its precision requirements, enabling low-precision quantization while maintaining accuracy through controlled parameter variation.
2Ease of manufacture
If precision is decreased in neural networks, then cost and processing efficiency are improved, but accuracy loss occurs
Solution Approach 1:
The patent implements preliminary action by pre-initializing the output limit parameter before training begins. This preliminary setup establishes a foundation for subsequent precision optimization. The system prepares the activation function parameters in advance, allowing the neural network to maintain accuracy even when operating with reduced precision during actual inference, thereby reducing cost without significant accuracy loss.
3Measurement precision
If output limit parameter is modified during training, then precision efficiency is improved, but training complexity increases
Solution Approach 1:
The patent applies dynamics by making the output limit parameter modifiable during the training process. Rather than using a fixed parameter, the system dynamically adjusts the output limit based on training progress and performance metrics. This dynamic approach allows the neural network to adapt its precision characteristics over time, improving precision efficiency while managing training complexity through controlled parameter evolution.
Data Source
AI summary
Techniques that facilitate improving an efficiency of a neural network are described. In one embodiment, a system is provided that comprises a memory that stores computer-executable components and a processor that executes computer-executable components stored in the memory. In one implementation, the computer-executable components comprise an initialization component that selects an initial value of an output limit, wherein the output limit indicates a range for an output of an activation function of a neural network. The computer-executable components further comprise a training component that modifies the initial value of the output limit during training to a second value of the output limit, the second value of the output limit being provided as a parameter to the activation function. The computer-executable components further comprise an activation function component that determines the output of the activation function based on the second value of the output limit as the parameter.