Sparsity-Inducing Regularization for Neural Network Model Compression
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Solution Overview
Problem
Existing model compression methods are often heuristic and may not achieve convergence, leading to potential sacrifices in testing accuracy, and are not easily applicable to various network architectures, requiring significant development effort for adaptation and often necessitating retraining on the compressed model.
Innovation Solution
A stochastic optimization algorithm, OBProx-SG, is introduced that uses sparsity-inducing regularization to achieve effective model compression by combining the benefits of orthant-based methods and proximal stochastic gradient methods, allowing for efficient compression without sacrificing accuracy and extending applicability to diverse architectures without the need for retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If heuristic model compression methods are used, then model size is reduced, but testing accuracy may be sacrificed and convergence is not achieved
Solution Approach 1:
The patent applies L1 regularization parameter to induce sparsity in model parameters, transforming the continuous parameter space into a sparse discrete structure. This parameter-based approach systematically reduces model size while maintaining accuracy through controlled shrinkage of parameter magnitudes, avoiding the accuracy loss associated with heuristic methods.
Solution Approach 2:
The patent replaces heuristic compression mechanisms with a formal optimization framework based on proximal stochastic gradient descent. This substitution introduces a mathematically grounded convergence guarantee, replacing ad-hoc compression techniques with a systematic optimization process that ensures both compression and accuracy preservation.
2Productivity
If existing compression algorithms are applied, then compression is achieved, but they are not easily applicable to other network architectures requiring significant development effort
Solution Approach 1:
The patent formulates a universal L1-regularized optimization framework that can be applied to any differentiable model architecture. The proximal stochastic gradient descent algorithm with L1 regularization is architecture-agnostic, enabling the same compression methodology to be deployed across diverse network types without requiring architecture-specific modifications or retraining.
3Volume of moving object
If model compression is performed, then model size is reduced and performance is improved, but processing time increases making compression time-consuming
Solution Approach 1:
The patent applies L1 regularization throughout the training process to pre-shrink parameter magnitudes and induce sparsity in advance. This preliminary sparsity induction eliminates the need for time-consuming post-compression processing steps, as the model is already in its compressed form upon completion of training, significantly reducing total compression time.
Solution Approach 2:
The patent integrates compression into the continuous training process through L1 regularization, maintaining useful action throughout. Rather than performing compression as a separate post-processing step, the compression occurs continuously during training, eliminating idle time and making the compression process as efficient as the training itself.
Data Source
AI summary
The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as management of data among the various memory components of the NN/DNN. A sparsity-inducing regularization optimization process is performed on a machine learning model to generate a compressed machine learning model. A machine learning model is trained using a first set of training data. A sparsity-inducing regularization optimization process is executed on the machine learning model. Based on the sparsity-inducing regularization optimization process, a compressed machine learning model is received. The compressed machine learning model is executed to generate one or more outputs.