Joint Pruning and Quantization for Smaller Neural Networks
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Solution Overview
Problem
Large neural networks require significant memory and processing resources, limiting their deployment on devices with modest capabilities, and existing pruning and quantization techniques often result in suboptimal performance when applied separately or sequentially.
Innovation Solution
A joint pruning and quantization method that integrates these processes, reducing redundant parameters and bit widths through iterative training, resulting in a smaller, more efficient neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If pruning and quantization are applied separately or sequentially, then the neural network size is reduced, but the performance becomes suboptimal
Solution Approach 1:
The patent merges pruning and quantization into a single joint optimization process. The system simultaneously performs both operations by integrating them into one training loop with shared computational graphs, allowing the model to learn optimal pruning patterns and quantization parameters together rather than sequentially, thereby achieving both size reduction and optimal performance
Solution Approach 2:
The patent changes the optimization parameters by introducing learnable quantization parameters (quantization step sizes, scaling factors) that are trained alongside the neural network weights. This allows the system to adaptively determine optimal quantization levels during training, ensuring that the reduced precision does not significantly impact performance while achieving the desired model compression
2Reliability
If large neural networks are used to maintain performance, then accuracy is improved, but memory and processing resources are excessive
Solution Approach 1:
The patent segments the neural network parameters into different groups and applies selective pruning and quantization to each segment. By dividing the model into manageable parts and applying compression techniques locally, the system achieves overall resource reduction while maintaining performance in critical segments, effectively balancing accuracy and resource consumption
Solution Approach 2:
The patent implements dynamic adaptation where the quantization parameters and pruning patterns are learned during training rather than being fixed. This allows the model to automatically adjust its representation to minimize resource usage while maintaining performance, creating a dynamic compression strategy that optimizes the trade-off between accuracy and resource consumption
Data Source
AI summary
A technique jointly prunes and quantizes an original neural network. This produces a final neural network having a smaller size than the original neural network. The technique involves converting the original neural network into a quantized-preprocessed neural network by adding weight-quantization logic that simulates effects of converting weight parameters used by the original neural network into quantized weight parameters, and/or adding activation-quantization logic that simulates effects of converting activations produced by the layers of the original neural network into quantized activation information. The technique then applies an iterative training process that includes: (a) identifying a prescribed number of groups of weight parameters in a set of original groups as redundant, a remainder of the original set of groups being to-be-retained groups, and (b) determining quantization parameters that will govern quantization used in levels of the final neural network.