CSR: Medium: Collaborative Research: Scale-Out Near-Data Acceleration of Machine Learning

越来越多的商业和企业系统越来越依赖于机器学习算法。一方面，这种转变是由于从大量数据中提取见解的机器学习算法的突破。因此，这样的系统需要处理不断增加的数据量，要求更高的内存带宽和容量。然而，由于各种严格的物理限制，处理器和片外存储器之间的带宽并没有增加。此外，由于互连和晶体管缩放之间的差异，处理器和片外存储器之间的数据传输消耗的能量比片上计算多几个数量级。利用最近的3d堆叠技术，研究人员社区已经探索了将处理器和内存放在同一芯片上的近数据处理架构。然而，由于3d堆叠技术的高成本以及对现有处理器、内存和/或应用程序的要求改变，目前尚不清楚这种内存处理（PIM）的尝试是否会在商品计算系统中成功。面对这些挑战，pi正在研究不需要对处理器、内存和应用程序进行任何更改的近数据处理平台，利用对商品内存子系统和网络软件堆栈的深刻见解。这个项目的成功将产生廉价但功能强大的近数据处理平台，可以直接运行现有的机器学习应用程序，而无需任何修改。

A growing number of commercial and enterprise systems increasingly rely on machine learning algorithms. This shift is, on the one hand, due to the breakthroughs in machine learning algorithms that extract insights from massive amounts of data. Therefore, such systems need to process ever-increasing amounts of data, demanding higher memory bandwidth and capacity. However, the bandwidth between processors and off-chip memory has not increased due to various stringent physical constraints. Besides, data transfers between the processors and the off-chip memory consume orders of magnitude more energy than on-chip computation due to the disparity between interconnection and transistor scaling.Exploiting recent 3D-stacking technology, the researcher community has explored near-data processing architectures that place processors and memory on the same chip. However, it is unclear whether or not such processing-in-memory (PIM) attempts will be successful for commodity computing systems due to the high cost of 3D-stacking technology and demanded change in existing processor, memory and/or applications. Faced with these challenges, the PIs are to investigate near-data processing platforms that do not require any change in processor, memory and applications, exploiting deep insights on commodity memory subsystems and network software stack. The success of this project will produce inexpensive but powerful near-data processing platforms that can directly run existing machine learning applications without any modification.