AI Error Correction for AR Memory in Hazardous Conditions
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Solution Overview
Problem
Existing memory devices face challenges in error correction, particularly in hazardous conditions and overclocking scenarios, where hardware-based error correction methods are insufficient in correcting multiple bit errors and increase hardware overhead, contradicting the resource constraints of smart devices.
Innovation Solution
Implementing software-based error correction using AI circuitry, such as neural networks trained to emulate error correction models, which can correct multiple bits and operate in conjunction with or instead of hardware-based error correction mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardware-based error correction (ECC) is used, then error correction capability is improved, but hardware overhead and resource consumption increase
Solution Approach 1:
The patent replaces hardware-based error correction mechanisms with a software-based AI neural network model. The neural network is trained to detect and correct bit errors in memory data, substituting traditional hardware ECC circuits with an AI-driven software solution that achieves comparable or superior error correction capability without the associated hardware overhead.
Solution Approach 2:
The patent changes the operational parameters by training the neural network on specific error patterns and memory characteristics. The model learns from training data to optimize its error correction performance for particular memory types and error conditions, allowing the software system to adapt to different hardware configurations without requiring hardware-specific customization.
2Reliability
If additional specialized circuitry is added for hazardous conditions, then reliability in hazardous environments is improved, but hardware overhead increases
Solution Approach 1:
The patent substitutes specialized hardware circuitry designed for hazardous environments with a software-based neural network model. The AI model is trained to recognize and correct errors specifically caused by hazardous conditions such as radiation, extreme temperatures, or overclocking, providing targeted error correction without requiring dedicated hardware protection circuits for each hazard type.
Solution Approach 2:
The neural network model serves multiple functions: it corrects errors from various hazardous conditions, operates in different memory configurations, and can be applied across multiple device types. This universal approach replaces the need for separate specialized circuitry for each hazard or application scenario.
3Reliability
If hardware-based error correction is used, then error correction capability is improved, but power consumption increases
Solution Approach 1:
The patent replaces power-intensive hardware ECC circuits with a software-based neural network implementation. The AI model processes error correction through computational algorithms that consume significantly less power than dedicated hardware correction circuits, particularly when running on modern processors with efficient neural network inference capabilities.
Solution Approach 2:
The neural network model can be optimized to apply error correction only when necessary, based on the specific error conditions detected. The model processes data in a manner that applies correction only to affected portions, avoiding the continuous power consumption associated with always-active hardware ECC circuits.
Data Source
AI summary
Apparatuses and methods related to error correction via artificial intelligence (AI) are described. An augmented reality (AR) display can be coupled to a memory device. AI circuitry coupled to the memory device can receive an error correction model. Prior to receipt of the error correction model by the AI circuitry, the error correction model can be trained, externally to the memory device and AI circuitry, to correct random errors introduced to execution of the AR AI workload in hazardous conditions. The AI circuitry can execute the model to perform error correction in association with execution of the AR AI workload.