SPX: Scalable In-Memory Processing Using Spintronics

现代工作负载的计算需求受到以数据为中心的计算观点的影响。传统的计算模型将数据带入计算引擎进行处理，随着数据传输的开销变得令人生畏，在数据量爆炸的时代，这种计算模型正在崩溃。相反，对数据进行计算更有利。这个项目的目标是探索通过开发一个新的可扩展的内存处理（PIM）框架，将计算带到数据的替代范式。虽然传统的CMOS结构不适合这种紧密集成，但新兴的自旋电子技术在这方面表现出显着的多功能性。所提出的方法将开发的概念的计算RAM（CRAM）建立PIM解决方案，以解决数据密集型计算问题，使用自旋电子技术。该项目旨在为CRAM平台下的PIM问题提供跨系统堆栈的完整解决方案。该项目旨在推进电子技术的最新发展，并可能对普遍电子化的社会产生重大影响。从技术上讲，其研究成果预计将大大推进使用后CMOS自旋电子技术的大规模以存储器为中心的计算的最新技术水平，为构建节能，可扩展的集成系统的新方法铺平道路。将采取多管齐下的外联战略，将这一努力的成果传达给一系列核心支持者。人力资源开发将通过对本科生和研究生进行后CMOS方法和新型计算模式的培训来实现。然而，由于大型存储器阵列的规则性被认为是神圣不可侵犯的，因此迄今为止提出的最可行的解决方案执行近存储器处理，在大型存储器阵列的边缘执行计算。所提出的基于CRAM的方法避免了这种方法在将数据带到外围和从外围带来数据时的大量开销，并且提出了一种用于重新配置存储器以将逻辑操作的输出直接写入存储器单元的方法。该项目实现了CRAM在整个系统堆栈上的潜力，通过探索在技术、逻辑设计和存储器架构方面的最佳选择，以实现一组不同的基本计算构建块;通过定量表征CRAM特定的多粒度并行;通过调查生态系统集成的影响;通过设计CRAM特定的时空并行任务调度的有效方法;并通过展示生物信息学应用和具有不规则性的应用，即，无定形并行性可受益于CRAM。

The computational demands of modern workloads are influenced by a data-centric view of computing. The traditional model of computing, which brought the data into the compute engine for processing, is falling apart in the era of exploding data volumes as the overheads of data transportation become forbidding. Instead, it is more advantageous to take computing to the data. The objective of this project is to explore the alternative paradigm of bringing computation to the data by developing a novel scalable framework for processing-in-memory (PIM). While traditional CMOS structures are unsuited to this tight integration, emerging spintronic technologies show remarkable versatility in this regard. The proposed approach will develop the notion of the computational RAM (CRAM) to build PIM solutions to solve data-intensive computing problems using spintronics technologies. The project seeks to provide a complete solution across the system stack to the PIM problem under the CRAM platform. The project seeks to advance the state of the art in electronics technology, and potentially has a large impact in a pervasively-electronic society. Technically, its research results are projected to significantly advance the state of the art in large scale memory-centric computing using post-CMOS spintronic technologies, paving the way for new ways to build energy-efficient, scalable integrated systems. A multi-pronged outreach strategy will be pursued to take the results of this effort to a set of core constituencies. Human resource development will be achieved by training of undergraduate and graduate students in post-CMOS methods and novel computing paradigms.The notion of bringing computation nearer to memory has gained wide currency in the recent past. However, since the regularity of large memory arrays is considered sacrosanct, the most viable solutions proposed so far perform processing near-memory, performing computation at the edge of a large memory array. The proposed CRAM-based approach avoids the substantial overheads of such a method, in bringing data to and from the periphery, and proposes a method for reconfiguring the memory to write the output of a logic operation directly into a memory cell. This project realizes the potential of the CRAM across the system stack by exploring the optimum over a space of choices in technology, logic design, and memory architecture to implement a diverse set of basic computational building blocks; by quantitatively characterizing CRAM-specific multi-granular parallelism; by investigating implications for the eco-system integration; by devising effective methods for CRAM-specific spatio-temporal parallel task scheduling; and by demonstrating how bioinformatics applications and applications featuring irregular, i.e., amorphous parallelism can benefit from CRAM.