Secure memory systems employing AES-CTR encryption face significant performance challenges due to high counter (CTR) cache miss rates, especially in applications with irregular memory access patterns.These high miss rates increase memory traffic and latency, as each CTR cache miss triggers additional DRAM accesses.To address these bottlenecks and adapt to diverse access patterns, we propose COSMOS (Counter Optimized Secure Memory Operation Scheme), a novel solution leveraging reinforcement learning to reduce long memory access latency.COSMOS integrates two RL-based specialized predictors: one for data location prediction and another for CTR locality prediction, each with a well-defined state space, action space, and reward function.The RL-based data location predictor determines whether data reside on-chip or offchip after an L1 cache miss, enabling early CTR access for off-chip predictions with minimal changes to the existing cache hierarchy.The RL-based CTR locality predictor identifies CTRs with high locality, supporting a locality-centric CTR cache (LCR-CTR) to improve cache efficiency and reduce miss rates.COSMOS improves performance over MorphCtr by 25% in for irregular memory access applications, with minimal hardware overhead.

Background Patients’ continuous use of intelligent medical guidance is important for the application of artificial intelligence (AI) in hospitals.Problem/gap Previous studies mainly focus on the initial adoption while not examining why patients continually use AI-based medical services.Objective/aim We aim to explore the factors influencing patients’ continuous use of intelligent medical guidance based on the Stimuli-Organism-Response framework.Method Via online survey data, we used structural equation modelling to test the research model.Results Our results reveal feature diversity of intelligent medical guidance affect users’ perceived extrinsic and intrinsic value, as well as their impact on their continuous usage intention.Research implications Our research expands the understanding of medical AI applications and provides theoretical and implicational insights for future development.