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。传统的计算模式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望而却步，在数据爆炸式增长的时代，这种模式正在分崩离析。取而代之的是，将计算用于数据更为有利。这个项目的目标是探索通过开发一种新的可伸缩的内存中处理框架(PIM)来将计算引入数据的替代范式。虽然传统的cmos结构不适合这种紧密的集成，但新兴的自旋电子技术在这方面表现出了惊人的多功能性。提出的方法将发展计算RAM(CRAM)的概念，以构建PIM解决方案，以使用自旋电子学技术解决数据密集型计算问题。该项目寻求为CRAM平台下的PIM问题提供跨系统堆栈的完整解决方案。该项目寻求推动电子技术的发展，并可能在一个无处不在的电子社会产生巨大影响。从技术上讲，该公司的研究成果预计将显著推动使用后CMOS自旋电子技术的大规模以内存为中心的计算技术的发展，为构建节能、可扩展的集成系统铺平道路。将采取多管齐下的外展战略，将这一努力的成果带给一系列核心选民。人力资源开发将通过对本科生和研究生进行后CMOS方法和新的计算范例的培训来实现。使计算更接近记忆的概念在最近得到了广泛的流行。然而，由于大型存储器阵列的规律性被认为是神圣不可侵犯的，到目前为止提出的最可行的解决方案是在存储器附近执行处理，在大型存储器阵列的边缘执行计算。所提出的基于CRAM的方法在将数据带到外围设备和从外围设备接收数据方面避免了这种方法的大量开销，并且提出了一种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。该项目通过在技术、逻辑设计和存储器体系结构中的最佳选择空间来实现一组不同的基本计算构建块；通过定量描述CRAM特定的多粒度并行性；通过调查生态系统集成的影响；通过设计用于CRAM特定的时空并行任务调度的有效方法；以及通过展示具有不规则的、即无定形并行的生物信息学应用程序如何从CRAM中受益，从而实现了CRAM在整个系统堆栈中的潜力。

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.