Adaptive Memory Training for Automotive Boot Optimization
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Solution Overview
Problem
Conventional memory training in automotive systems forces an inflexible tradeoff between boot time, defect coverage, and performance, particularly in autonomous vehicles, as it is sensitive to environmental conditions like temperature and humidity, leading to compromised boot times and reduced memory performance over time.
Innovation Solution
The solution involves creating and storing multiple memory training data sets to match current operating conditions, allowing for optimized read/write timing and voltage settings, which are stored in non-volatile memory and used during system operation to enhance boot time and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional memory training is performed during automotive boot, then memory parameters are initialized, but boot time increases and performance is compromised
Solution Approach 1:
The patent performs memory training during a shutdown procedure before the automotive system is fully powered off, rather than during the boot process. This preliminary action during shutdown allows the trained memory parameters to be saved to non-volatile storage, eliminating the need for time-consuming memory training during subsequent boot operations.
Solution Approach 2:
The patent creates a copy of the trained memory parameters and stores them in non-volatile storage. During boot operations, the system loads these pre-trained parameters from storage rather than performing training from scratch, significantly reducing boot time while maintaining memory initialization reliability.
2Reliability
If memory training is performed frequently due to environmental changes, then memory performance is maintained, but system robustness is reduced
Solution Approach 1:
The system performs comprehensive memory training during shutdown procedures, preparing the memory parameters in advance before environmental changes occur. This preliminary training with sufficient time resources creates robust parameter sets that can withstand environmental variations without requiring frequent retraining.
Solution Approach 2:
The patent monitors environmental parameters such as temperature and determines when memory retraining is necessary based on threshold comparisons. This parameter-based approach allows the system to adapt to environmental changes only when necessary, maintaining memory performance consistency while avoiding excessive retraining that would reduce system robustness.
Data Source
AI summary
A computerized component, designed to be used in a vehicle, is able to detect an ambient environmental variable; determine a memory profile corresponding to the ambient environmental variable; access memory parameters from the memory profile; and configure memory of the computerized component based on the accessed memory parameters. Another computerized component may be used to detect that the computerized component is initiating a shutdown procedure; obtain an ambient environmental variable, the ambient environmental variable indicating a state of an operating environment of the computerized component; identify memory parameters of random access memory integrated with the computerized component; and write the memory parameters to a memory profile stored in non-volatile storage in the computerized component, the memory profile keyed to the ambient environmental variable.