The resurgence of near-memory processing (NMP) with the advent of big data has shifted the computation paradigm from processor-centric to memory-centric computing. To meet the bandwidth and capacity demands of memory-centric computing, 3D memory has been adopted to form a scalable memory-cube network. Along with NMP and memory system development, the mapping for placing data and guiding computation in the memory-cube network has become crucial in driving the performance improvement in NMP. However, it is very challenging to design a universal optimal mapping for all applications due to unique application behavior and intractable decision space. In this paper, we propose an artificially intelligent memory mapping scheme, AIMM, that optimizes data placement and resource utilization through page and computation remapping. Our proposed technique involves continuously evaluating and learning the impact of mapping decisions on system performance for any application. AIMM uses a neural network to achieve a near-optimal mapping during execution, trained using a reinforcement learning algorithm that is known to be effective for exploring a vast design space. We also provide a detailed AIMM hardware design that can be adopted as a plugin module for various NMP systems. Our experimental evaluation shows that AIMM improves the baseline NMP performance in single and multiple program scenario by up to 70% and 50%, respectively.
翻译:随着大数据的出现,近模处理(NMP)的死灰复燃随着大数据的出现,使计算范式从处理器中心转向内存中心计算。为满足内存中心计算对带宽和能力的需求,采用了三维内存来形成一个可缩放的内存-立方体网络。随着NMP和记忆系统开发,在内存-立方体网络中放置数据和指导计算图的映射对于推动NMP的性能改进至关重要。然而,由于独特的应用行为和棘手的决策空间,设计一个所有应用的通用最佳绘图非常困难。在本文中,我们提出了一个人工智能的内存映射计划,即AIMM,通过页面和计算再映射优化数据定位和资源利用。我们提出的技术涉及不断评估和学习测绘决定对任何应用系统性能的影响。AIMMM在实施期间使用神经网络实现近于最佳的绘图,经过培训后使用一种强化学习算法来有效探索广阔的设计空间。我们还提供了一套详细的AIMM硬件设计,可以作为各种NMP系统的插件模块。我们提出的实验性评估方案分别改进了IMMM的70和NP的单一模型。