ANN Training for Photonic Accelerator MTTF Predictions
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Solution Overview
Problem
Current physics solvers used to generate mean time to failure (MTTF) predictions for photonic accelerators are computationally expensive and time-consuming, limiting the ability to make timely decisions about device retirement and resource management.
Innovation Solution
Training artificial neural networks (ANNs) to replace physics solvers and MTTF heuristics, allowing for faster generation of device throughput characteristics and MTTF predictions, which can then be used to determine device age and retirement timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physics solvers are used to generate MTTF predictions, then prediction accuracy is improved, but computation time and resource consumption increase
Solution Approach 1:
The patent pre-generates device throughput characteristics using physics solvers during an offline training phase, storing these pre-computed results in a dataset. This preliminary action allows the trained ANN to make rapid predictions during online operation without requiring real-time physics solver computations, thus resolving the contradiction between accuracy and computation time.
Solution Approach 2:
The patent creates a trained ANN model that copies the predictive capabilities of the physics solver. The ANN learns to replicate the physics solver's MTTF predictions by training on pre-computed data, enabling fast inference that mimics the accurate but slow physics-based calculations, thereby achieving both speed and accuracy.
2Measurement precision
If physics solvers are used to generate device throughput characteristics, then prediction accuracy is improved, but computational resources consumed increase
Solution Approach 1:
The patent performs computationally intensive physics solver calculations in advance during the training phase, storing results in a pre-generated dataset. This shifts the computational burden from real-time operation to an offline setup phase, allowing resource-efficient predictions during actual device management operations while maintaining accuracy through the trained ANN model.
Solution Approach 2:
The trained ANN model serves as a lightweight copy of the physics solver's predictive functionality. Once trained on accurate physics-based data, the ANN can replicate these predictions with minimal computational resources, eliminating the need for expensive real-time physics simulations during deployment.
3Productivity
If device retirement is delayed, then resource utilization is improved, but device reliability decreases
Solution Approach 1:
The patent implements a feedback mechanism where the trained ANN continuously predicts device throughput characteristics and MTTF based on current operation parameters. These predictions provide feedback on device aging and health status, enabling dynamic retirement decisions that optimize the balance between extending device usage for resource utilization and retiring before failure for reliability maintenance.
Data Source
AI summary
Training an artificial neural network (ANN) can include receiving device design parameters corresponding to a device and operation parameters corresponding to the device. Device throughput characteristics can also be received from a physics solver. Device throughput predictions can be generated utilizing the device design parameters, the operation parameters, and an artificial neural network. A loss gradient can be generated utilizing the device throughput characteristics and the device throughput predictions. The ANN can be trained, utilizing the loss gradient, to generate different device throughput predictions.