Distributed AI systems face critical memory management challenges across computation, communication, and deployment layers. RRAM based in memory computing suffers from scalability limitations due to device non idealities and fixed array sizes. Decentralized AI frameworks struggle with memory efficiency across NAT constrained networks due to static routing that ignores computational load. Multi agent deployment systems tightly couple application logic with execution environments, preventing adaptive memory optimization. These challenges stem from a fundamental lack of coordinated memory management across architectural layers. We introduce Self Evolving Distributed Memory Architecture for Scalable AI Systems, a three layer framework that unifies memory management across computation, communication, and deployment. Our approach features (1) memory guided matrix processing with dynamic partitioning based on device characteristics, (2) memory aware peer selection considering network topology and computational capacity, and (3) runtime adaptive deployment optimization through continuous reconfiguration. The framework maintains dual memory systems tracking both long term performance patterns and short term workload statistics. Experiments on COCO 2017, ImageNet, and SQuAD show that our method achieves 87.3 percent memory utilization efficiency and 142.5 operations per second compared to Ray Distributed at 72.1 percent and 98.7 operations per second, while reducing communication latency by 30.2 percent to 171.2 milliseconds and improving resource utilization to 82.7 percent. Our contributions include coordinated memory management across three architectural layers, workload adaptive resource allocation, and a dual memory architecture enabling dynamic system optimization.

翻译：分布式AI系统在计算、通信和部署层面面临严峻的内存管理挑战。基于RRAM的内存计算因器件非理想特性和固定阵列规模而存在可扩展性限制。去中心化AI框架在NAT约束网络中由于静态路由忽略计算负载而面临内存效率问题。多智能体部署系统将应用逻辑与执行环境紧密耦合，阻碍了自适应内存优化。这些挑战源于各架构层面缺乏协调的内存管理机制。我们提出面向可扩展AI系统的自演进分布式内存架构，这是一个统一计算、通信和部署三层内存管理的框架。本方法具有以下特点：（1）基于器件特性的动态分区内存引导矩阵处理技术；（2）考虑网络拓扑和计算容量的内存感知对等节点选择策略；（3）通过持续重配置实现运行时自适应部署优化。该框架维护双内存系统，同步追踪长期性能模式与短期工作负载统计。在COCO 2017、ImageNet和SQuAD数据集上的实验表明，相较于Ray Distributed的72.1%内存利用率和98.7次/秒操作速度，本方法实现87.3%内存利用效率和142.5次/秒操作速度，同时将通信延迟降低30.2%至171.2毫秒，并将资源利用率提升至82.7%。我们的贡献包括：跨三架构层面的协调内存管理、工作负载自适应资源分配，以及支持动态系统优化的双内存架构。