Recent advances in 3D fabrication have allowed handling the memory bottlenecks for modern data-intensive applications by bringing the computation closer to the memory, enabling Near Memory Processing (NMP). Memory Centric Networks (MCN) are advanced memory architectures that use NMP architectures, where multiple stacks of the 3D memory units are equipped with simple processing cores, allowing numerous threads to execute concurrently. The performance of the NMP is crucially dependent upon the efficient task offloading and task-to-NMP allocation. Our work presents a multi-armed bandit (MAB) based approach in formulating an efficient resource allocation strategy for MCN. Most existing literature concentrates only on one application domain and optimizing only one metric, i.e., either execution time or power. However, our solution is more generic and can be applied to diverse application domains. In our approach, we deploy Upper Confidence Bound (UCB) policy to collect rewards and eventually use it for regret optimization. We study the following metrics: instructions per cycle, execution times, NMP core cache misses, packet latencies, and power consumption. Our study covers various applications from PARSEC and SPLASH2 benchmarks suite. The evaluation shows that the system's performance improves by ~11% on average and an average reduction in total power consumption by ~12%.

翻译：近年来，三维制造技术的进步使得计算更接近内存，从而能够处理现代数据密集型应用中的内存瓶颈，催生了近内存处理（NMP）技术。内存中心网络（MCN）是一种采用NMP架构的先进内存架构，其中多个三维存储单元堆叠配备简单处理核心，支持大量线程并发执行。NMP的性能关键取决于高效的任务卸载及任务到NMP的分配。本研究提出了一种基于多臂赌博机（MAB）的方法，用于制定MCN的高效资源分配策略。现有文献大多仅关注单一应用领域，并仅优化单一指标（即执行时间或功耗）。然而，我们的解决方案更具通用性，可应用于多种应用领域。该方法采用上置信界（UCB）策略收集奖励，并最终用于遗憾优化。我们研究了以下指标：每周期指令数、执行时间、NMP核心缓存未命中率、数据包延迟及功耗。研究涵盖了来自PARSEC和SPLASH2基准测试套件的多种应用。评估结果显示，系统性能平均提升约11%，总功耗平均降低约12%。