2025 ASEE Annual Conference & Exposition

A significant challenge with non-volatile memory (NVM) is the uneven wear resulting from frequent write operations concentrated on specific memory locations. This problem can drastically reduce the memory's lifespan and compromise system reliability. Frequent writes to certain cells accelerate wear-out, causing errors and degraded performance. Managing this wear in NVM systems is crucial to ensuring their long-term efficiency and usability. In this research, we propose a novel solution called Predictive Memory Wear Balancing (PMWB) to address these challenges. PMWB effectively mitigates the wear imbalance problem. Moreover, PMWB integrates dynamic approximation adjustments, making it one of the first approaches to combine wear balancing with adaptive approximation techniques, which further extend memory endurance without significantly affecting system performance. The PMWB mechanism operates in two stages: wear prediction and dynamic write redistribution. First, the system monitors the memory’s wear status in real time, tracking how often specific cells are written to and assessing their wear-out progression. This data is used to predict future wear patterns and inform the system where redistribution is needed. Secondly, the system redistributes write traffic away from heavily worn cells ultimately, extending the overall lifespan of the memory. PMWB leverages adaptive machine learning models, particularly reinforcement learning (RL), to monitor and predict memory wear-out patterns. Reinforcement learning is ideal for this problem because it allows the system to learn optimal strategies for balancing memory wear out over time. In PMWB, the memory system is treated as an environment, and the RL agent interacts with it by taking actions (like redistributing write operations or adjusting approximation levels) to maximize a reward. In this scenario, the reward is a prolonged memory lifespan, and the RL agent is trained to optimize this goal by balancing wear across memory cells. The RL algorithm learns from experience by observing the wear patterns and adjusting its strategy dynamically. As the memory system undergoes more writes, the RL model refines its predictions of the cells likely to wear out first. Based on this predictive modeling, it redistributes write operations to less worn cells, thus balancing the wear across the entire memory. This delays the onset of errors and also ensures a more consistent performance over the memory’s lifetime. PMWB’s integration of approximation tuning is another innovative aspect. For applications where perfect accuracy is not always required such as multimedia processing, this trade-off is beneficial. The adaptive nature of PMWB’s reinforcement learning model means that it continuously improves over time, refining its predictions and redistributions as it encounters new write patterns. This adaptability is essential in memory systems where usage patterns may fluctuate due to workload. In fact, PMWB offers a robust and intelligent solution to the problem of uneven wear in non-volatile memory systems. By combining reinforcement learning-based wear prediction with dynamic approximation tuning, it extends memory endurance while preserving system performance. This novel approach represents a significant advancement in memory wear management, particularly for systems leveraging approximate computing, and has the potential to enhance the longevity and reliability of future NVM technologies.