Neural Network Loop Detection for Resource Optimization
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Solution Overview
Problem
Evaluating the output of neural networks is resource-intensive in terms of time and memory, making it inefficient.
Innovation Solution
A graph-based model is used to represent and optimize neural networks through loop reconstruction and optimization techniques, such as loop fission, fusion, parallelization, and vectorization, to improve performance by centralizing data flow and identifying and reconstructing control flow patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neural network evaluation is performed, then accurate output evaluation is achieved, but significant time and memory resources are consumed
Solution Approach 1:
The patent segments the neural network computation into distinct operational phases and identifies loop structures within the computation graph. By detecting loops and separating them into identifiable regions, the system can apply specific optimizations to different segments, reducing redundant computations while preserving evaluation accuracy.
Solution Approach 2:
The patent performs loop detection and reconstruction as a preliminary action before executing the full neural network evaluation. By pre-identifying loop structures and preparing optimized computation paths in advance, the system avoids redundant computations during actual evaluation, significantly reducing evaluation time while maintaining accuracy.
2Measurement precision
If traditional neural network evaluation is performed, then complete output assessment is achieved, but excessive memory resources are required
Solution Approach 1:
The computation graph is segmented into loop regions and non-loop regions, allowing memory-intensive operations to be identified and optimized separately. This segmentation enables the system to manage memory resources more efficiently by focusing optimization efforts on specific high-memory-consumption areas.
Solution Approach 2:
The patent applies loop optimizations that enable discarding of intermediate computation results that can be regenerated, and recovering computational patterns that can be reused. This approach reduces the memory footprint by eliminating the need to store all intermediate results while maintaining evaluation completeness.
3Power
If loop reconstruction and optimization techniques are applied, then processor utilization is enhanced, but device complexity increases
Solution Approach 1:
The system performs self-service by automatically detecting loops and applying optimizations without requiring external intervention or complex configuration. The loop detection mechanism autonomously analyzes the computation graph, identifies optimization opportunities, and applies appropriate transformations, simplifying the overall system architecture while improving processor utilization.
Data Source
AI summary
Apparatuses, systems, and techniques to detect loops in neural network graphs. In at least one embodiment, one or more loops are detected within one or more graphs corresponding to one or more neural networks.