Neural Meta-Optimization of Experts for Low-Latency Complex Optimization
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Solution Overview
Problem
Optimization techniques for complex problems are computationally complex and resource-intensive, limiting their application on devices with limited computational resources and high latency requirements, such as mobile devices.
Innovation Solution
A meta-optimization neural network that processes outputs from existing expert models to generate optimized solutions, incorporating loss functions and learnable parameters to improve efficiency and accuracy, allowing for faster and more accurate optimization without the need for extensive computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optimization techniques are used to solve complex optimization problems, then solution accuracy is improved, but computational complexity and resource consumption increase significantly
Solution Approach 1:
The patent segments the optimization problem into two distinct phases: (1) an expensive but accurate optimization step performed offline to generate training data, and (2) a lightweight neural network inference step performed online for rapid predictions. This segmentation allows the complex optimization to be done once during training, while deployment uses only the lightweight neural network, thus resolving the contradiction between accuracy and computational complexity.
Solution Approach 2:
The patent performs preliminary optimization computations during the training phase to create a dataset of input-output pairs. The neural network learns from these pre-computed optimal solutions, so that during deployment, no actual optimization computations are needed - only fast neural network inference. This preliminary action transfers the computational burden from deployment to training time.
2Measurement precision
If traditional optimization techniques are applied to complex problems, then solution quality is improved, but processing time and latency increase
Solution Approach 1:
The patent separates the time-consuming optimization computations from the time-critical inference operations. Optimization is performed offline during training to generate high-quality training data, while inference uses fast neural network forward propagation. This temporal segmentation resolves the contradiction between solution quality and processing time.
Solution Approach 2:
The patent creates a neural network copy that approximates the behavior of the expensive optimization algorithm. Instead of running the original optimization algorithm during deployment, the system uses the trained neural network copy that has learned to predict optimal solutions, achieving similar quality with much faster processing.
3Productivity
If complex optimization algorithms are used, then optimization performance is improved, but resource consumption (processor power, memory, bandwidth) increases
Solution Approach 1:
The patent replaces the mechanical optimization process (iterative numerical computations requiring significant CPU/GPU resources) with a neural network-based system. The neural network, once trained, requires minimal computational resources for inference, thus substituting the resource-intensive optimization mechanism with a lightweight predictive model.
Solution Approach 2:
The patent divides the computational workload into a resource-intensive training phase and a resource-light inference phase. During training, full optimization performance is achieved through expensive computations, but during deployment, only lightweight neural network evaluation is needed, thus resolving the contradiction between optimization performance and resource consumption.
Data Source
AI summary
Example systems perform complex optimization tasks with improved efficiency via neural meta-optimization of experts. In particular, provided is a machine learning framework in which a meta-optimization neural network can learn to fuse a collection of experts to provide a predicted solution. Specifically, the meta-optimization neural network can learn to predict the output of a complex optimization process which optimizes over outputs from the collection of experts to produce an optimized output. In such fashion, the meta-optimization neural network can, after training, be used in place of the complex optimization process to produce a synthesized solution from the experts, leading to orders of magnitude faster and computationally more efficient prediction or problem solution.