Memory-centric computing aims to enable computation capability in and near all places where data is generated and stored. As such, it can greatly reduce the large negative performance and energy impact of data access and data movement, by fundamentally avoiding data movement and reducing data access latency & energy. Many recent studies show that memory-centric computing can greatly improve system performance and energy efficiency. Major industrial vendors and startup companies have also recently introduced memory chips that have sophisticated computation capabilities. This talk describes promising ongoing research and development efforts in memory-centric computing. We classify such efforts into two major fundamental categories: 1) processing using memory, which exploits analog operational properties of memory structures to perform massively-parallel operations in memory, and 2) processing near memory, which integrates processing capability in memory controllers, the logic layer of 3D-stacked memory technologies, or memory chips to enable high-bandwidth and low-latency memory access to near-memory logic. We show both types of architectures (and their combination) can enable orders of magnitude improvements in performance and energy consumption of many important workloads, such as graph analytics, databases, machine learning, video processing, climate modeling, genome analysis. We discuss adoption challenges for the memory-centric computing paradigm and conclude with some research & development opportunities.

翻译：以内存为核心的计算旨在实现数据生成与存储位置附近的计算能力。通过从根本上避免数据搬运并减少数据访问延迟与能耗，该方法可大幅降低数据访问与移动对性能及能耗的负面影响。近期多项研究表明，以内存为核心的计算能显著提升系统性能与能效。主要工业厂商及初创企业也已推出具备复杂计算能力的内存芯片。本报告将介绍以内存为核心的计算领域中有前景的现有研发现状。我们将此类研发工作分为两大基本类别：1) 利用内存进行计算——利用内存结构的模拟运算特性执行内存中的大规模并行运算；2) 内存附近计算——在内存控制器、3D堆叠内存技术的逻辑层或内存芯片中集成处理能力，使近内存逻辑实现高带宽、低延迟的内存访问。研究表明，两种架构（及其组合）可在图分析、数据库、机器学习、视频处理、气候建模、基因组分析等重要负载领域实现性能与能耗的数量级提升。最后，我们将讨论以内存为核心的计算范式面临的采纳挑战，并总结相关研发机遇。