Inverse Square Root Operator for Neural Network Learning Efficiency
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Solution Overview
Problem
Neural network optimization algorithms, such as Gradient Descent, face challenges in reaching Global Minima due to high computational complexity and power consumption, especially when using mini-batch Stochastic Gradient Descent, which can settle at Local Minima and require extensive processing time.
Innovation Solution
A neural network learning apparatus that incorporates an inverse square root operator using a Newton-Raphson method and linear approximation for the logarithmic function, transforming the cumulative change function of the gradient into an inverse square root function, optimizing the neural network model with a learning processor that includes a shifter, integer subtractor, floating-point subtractor, and multiplier, reducing the need for special function units or dividers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Gradient Descent algorithm is used for neural network optimization, then the error function can be reduced by shifting weight in the direction of differentiating the error function, but the learning process takes a long time because it uses the entire data set
Solution Approach 1:
The patent segments the gradient computation process into multiple stages: computing gradients for individual data points, accumulating them, and applying the inverse square root transformation. This allows parallel processing of gradient computations across mini-batches while maintaining accuracy, thereby reducing overall learning time without sacrificing error function reduction effectiveness
2Productivity
If Stochastic Gradient Descent with mini-batch is used to speed up updates, then faster learning is possible, but the optimization may settle at Local Minima instead of reaching Global Minima
Solution Approach 1:
The patent implements a feedback mechanism through the inverse square root transformation of the cumulative gradient. This transformation dynamically adjusts the learning rate based on the magnitude of accumulated gradients, providing stabilizing feedback that helps the optimization process escape local minima while maintaining the speed benefits of mini-batch processing
Solution Approach 2:
The patent changes the parameter transformation approach by applying inverse square root to the cumulative gradient magnitude. This parameter transformation effectively modulates the learning rate adaptively, allowing faster convergence while maintaining optimization accuracy by preventing premature settling at local minima
3Measurement precision
If various neural network optimization algorithms (Momentum, NAG, Adagrad, RMSProp, Adam) are used to modify gradient and learning rate, then optimization performance can be improved, but enormous time is required due to algorithm complexity and iterative mini-batch calculation
Solution Approach 1:
The patent extracts and isolates the critical computational operation to just the inverse square root transformation of the cumulative gradient. By removing unnecessary complex iterative calculations found in traditional algorithms while retaining the essential adaptive learning rate mechanism, it achieves optimization performance with significantly reduced computational time
4Productivity
If parallel computing is used to handle the costly operational processing capability requirements, then processing speed can be improved, but hardware complexity and power consumption increase
Solution Approach 1:
The patent uses a simplified computational model that copies only the essential operations needed for gradient optimization. By replicating only the necessary calculation steps (gradient computation, accumulation, and inverse square root transformation) rather than implementing full parallel computing architectures, it achieves improved processing speed with reduced hardware complexity
Data Source
AI summary
Disclosed is a neural network learning apparatus for deep learning and a method thereof. A neural network learning apparatus for deep learning according to an embodiment of the present disclosure includes an input interface, a memory, and a learning processor for applying a Gradient Descent algorithm to a neural network model, and the learning processor may transform a cumulative change function of the gradient for an error function into an inverse square root function in the Gradient Descent algorithm, and operate an inverse square root approximate value by using a Newton-Raphson method for the transformed inverse square root function. The neural network learning apparatus for deep learning of the present disclosure may be connected or converged with an Artificial Intelligence module, an Unmanned Aerial Vehicle (UAV), a robot, an Augmented Reality (AR) apparatus, a Virtual Reality (VR), or a 5G network service-related apparatus, etc.