Neural Network Edge Utility Estimation for Pruning
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Solution Overview
Problem
Existing methods for pruning neural networks focus on weight magnitude rather than actual utility, leading to inefficient resource usage and computational expense in estimating edge utility for pruning and quantization.
Innovation Solution
A novel method for estimating the utility of neural network edges using a first-order approximation of the loss function, allowing for intelligent pruning and quantization decisions based on edge utility rather than weight magnitude, reducing resource consumption with minimal performance degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing pruning methods use absolute weight values to identify edges for removal, then the pruning process is simple and fast, but the method focuses on weight magnitude rather than actual utility, leading to inefficient resource usage and significant performance degradation
Solution Approach 1:
The patent introduces a cheap proxy metric (product of gradient magnitude and weight) to estimate edge utility instead of performing expensive full network re-evaluations. This disposable approximation allows rapid identification of removable edges without significant computational cost, resolving the contradiction between pruning efficiency and performance preservation
Solution Approach 2:
The patent changes the parameter used for edge selection from simple weight magnitude to a composite metric involving both gradient magnitude and weight. This parameter transformation enables the system to identify edges based on their actual contribution to loss rather than just their numerical value, improving both pruning efficiency and performance retention
2Measurement precision
If full network evaluation is performed to compute actual edge utility, then accurate utility measurement is achieved, but the computational expense becomes prohibitive for practical use
Solution Approach 1:
The patent introduces gradient magnitude as an intermediary metric that correlates with edge utility without requiring full network re-evaluation. This intermediary allows accurate utility estimation by combining gradient information with weight values, achieving measurement precision while avoiding the prohibitive computational cost of complete network evaluations
Solution Approach 2:
The patent replaces the mechanical process of full network re-evaluation with a mathematical approximation using gradient and weight products. This substitution eliminates the need for actual network execution to measure utility, dramatically reducing computational energy consumption while maintaining measurement accuracy
3Quantity of substance
If neural network size is reduced through aggressive pruning, then resource consumption decreases, but prediction performance degrades significantly
Solution Approach 1:
The patent transforms the pruning criterion from weight magnitude to utility-based measurement (gradient × weight product). This parameter change enables identification of truly non-contributing edges, allowing aggressive pruning that reduces network size while preserving prediction accuracy by targeting only edges with minimal actual utility
Data Source
AI summary
The present disclosure provides systems and methods for modification (e.g., pruning, compression, quantization, etc.) of artificial neural networks based on estimations of the utility of network connections (also known as “edges”). In particular, the present disclosure provides novel techniques for estimating the utility of one or more edges of a neural network in a fashion that requires far less expenditure of resources than calculation of the actual utility. Based on these estimated edge utilities, a computing system can make intelligent decisions regarding network pruning, network quantization, or other modifications to a neural network. In particular, these modifications can reduce resource requirements associated with the neural network. By making these decisions with knowledge of and based on the utility of various edges, this reduction in resource requirements can be achieved with only a minimal, if any, degradation of network performance (e.g., prediction accuracy).