Deep Learning Model Optimization via Sub-Graph Replacement
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Solution Overview
Problem
Existing deep learning (DL) frameworks fail to identify subtle optimization bottlenecks in model architectures, leading to sub-optimal performance and inefficient debugging experiences, as they either overlook these issues or require extensive computational resources to address them.
Innovation Solution
A computer-automated system uses a learned neural network and heuristics-based algorithms to identify sub-optimal combinations of operations in DL models, generating optimal replacements to improve expressivity without rebuilding the entire model, preserving user-encoded knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing DL frameworks provide unlimited freedom to compose operations, then model design flexibility is improved, but sub-optimal model performance occurs due to unintentional optimization bottlenecks
Solution Approach 1:
The system performs automated analysis of the composed model architecture to detect sub-optimal patterns and provides feedback to the engineer. This feedback loop allows the system to identify bottlenecks in operation composition and suggest corrections, thereby maintaining design flexibility while improving model performance and reliability.
2Measurement precision
If existing tools identify only extreme errors, then false positive rate is reduced, but subtle optimization bottlenecks are missed causing sub-optimal performance
Solution Approach 1:
The system segments the model analysis into multiple levels: extreme error detection (malformed graphs, NaN activations) and subtle optimization bottleneck detection (sub-optimal operation compositions). This segmentation allows the tool to maintain high precision in identifying actual errors while also detecting subtle performance issues that would otherwise be missed, thereby improving both detection accuracy and optimization efficiency.
3Reliability
If engineers manually optimize model architectures, then model performance can be improved, but extensive computational resources and time are required
Solution Approach 1:
The system enables automated self-service optimization by analyzing the model architecture and automatically identifying sub-optimal operation compositions. This eliminates the need for extensive manual optimization efforts, reducing both the time and computational resources required while maintaining improved model performance through automated detection and suggestion of optimal architectures.
4Ease of manufacture
If existing DL libraries allow loose constraints on operation combinations, then ease of model creation is improved, but optimization bottlenecks are unintentionally introduced
Solution Approach 1:
The system maintains loose constraints on operation combinations to preserve ease of model creation, while simultaneously providing automated feedback analysis that detects when sub-optimal compositions are introduced. This feedback mechanism allows engineers to freely compose operations without being constrained by complex rules, while the system identifies and flags optimization bottlenecks that arise from complex operation compositions.
Data Source
AI summary
Methods for optimizing a user model are disclosed. In at least some instances, the methods include receiving a user model and then identifying one or more sub-optimal combinations of operations in the user model. The methods further include generating a modification (e.g., an optimal subgraph) for at least one identified sub-optimal combination of operations in the user model and replacing that identified sub-optimal combination of operations with the generated modification for that identified sub-optimal combination of operations. Still further, the method includes returning the user model with the generated modification as a transformed model. Other methods, and systems for performing any methods disclosed, are also provided.