Artificial Neural Network Training Using Age-Based Error Injection
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Solution Overview
Problem
Memory devices, especially those used in deep learning applications, experience errors due to aging, which can lead to a decline in Quality of Service and render them unusable for deep learning tasks, as existing error correction mechanisms are insufficient to handle the increasing errors as the devices age.
Innovation Solution
The training of artificial neural networks (ANNs) is customized to account for age-related errors by introducing errors into the model, allowing the networks to function effectively even when inputs contain uncorrected errors, using techniques such as systematic noise in weights and biases to simulate aging effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing error correction mechanisms are used in memory devices, then initial reliability is maintained, but reliability deteriorates as the device ages due to insufficient error handling capability
Solution Approach 1:
The system performs preliminary training of the neural network to simulate and compensate for age-related errors before they actually occur. By pre-training the network to handle errors similar to those that will manifest as the device ages, the system prepares the model in advance to maintain accuracy throughout the device's operational lifespan, resolving the contradiction between initial reliability and long-term durability.
Solution Approach 2:
The system converts the harmful effect of aging into a beneficial training opportunity by using simulated age-related errors during the training phase. Instead of waiting for actual errors to occur and then attempting correction, the system uses the anticipated harmful effects of aging as training data to improve the network's robustness, thereby transforming the future harm into present benefit.
2Productivity
If memory devices are deployed in remote locations, then productivity is maintained, but loss of time increases due to costly maintenance and redeployment when faults occur
Solution Approach 1:
The system enables the neural network to self-compensate for errors without requiring external intervention or redeployment. By training the network to independently handle age-related errors using techniques like systematic noise in weights and biases, the system allows remote devices to maintain functionality autonomously, eliminating the need for time-consuming maintenance and redeployment operations.
3Reliability
If systematic noise is introduced into the neural network model during training, then the network becomes more robust to errors, but the complexity of the training process increases
Solution Approach 1:
The system modifies training parameters by introducing systematic noise into the weights and biases during the training process. By changing these parameters to include controlled error simulations that mirror actual hardware failures, the network learns to compensate for errors without requiring complex architectural changes or multiple training stages, thus improving robustness with manageable complexity.
Data Source
AI summary
Apparatuses and methods can be related to implementing age-based network training. An artificial neural network (ANN) can be trained by introducing errors into the ANN. The errors and the quantity of errors introduced into the ANN can be based on age-based characteristics of the memory device.