ADAS Boot Loader Adaptive Memory Pre-training

The patent applies preliminary action by performing complete memory training once during manufacturing or initial setup, then storing the trained parameters in non-volatile memory. During subsequent boot operations, the system loads these pre-trained parameters directly, eliminating the need to repeat the full training process. This resolves the contradiction by preparing memory parameters in advance, achieving both fast boot times and reliable memory operation without repeated training.

The patent uses copying by creating a copy of the trained memory parameters and storing them in non-volatile memory (such as flash memory or EEPROM). Instead of re-training memory during each boot, the system copies the essential parameter data from the original training session and reuses it. This copying mechanism allows the system to maintain memory reliability while dramatically reducing boot time, as copying data is much faster than performing full memory training.

The patent implements periodic action by scheduling memory re-training to occur at specific intervals rather than at every boot. The system monitors usage patterns, temperature conditions, and error rates, then triggers re-training only when environmental conditions warrant it or when a predetermined time interval has elapsed. This periodic approach maintains adaptability to environmental changes while preserving system productivity by avoiding unnecessary re-training operations during normal operation.

The patent applies dynamics by making the memory training process adaptive and condition-based rather than static and routine. The system dynamically adjusts whether to perform full training, partial training, or use cached parameters based on real-time conditions such as temperature extremes, voltage variations, detected errors, and operational history. This dynamic approach optimizes the balance between adaptability and productivity by applying training only when environmentally necessary.

The patent applies segmentation by dividing the memory training process into distinct phases: a complete initial training phase performed once to establish accurate parameters, and subsequent accelerated phases that load only essential parameters from non-volatile storage. The segmentation allows the system to achieve high parameter accuracy during the initial comprehensive training while maintaining fast boot speeds in subsequent operations by skipping redundant training steps and directly loading the segmented parameter sets.

The patent uses partial action by performing complete memory training once to ensure full parameter accuracy, then using only the essential portion of those trained parameters for subsequent rapid boots. The system loads a subset of critical parameters from non-volatile memory rather than repeating the full training sequence, achieving sufficient accuracy for normal operation while dramatically improving boot speed. Partial re-training may be performed intermittently to refresh parameters without the overhead of complete training.