Customizable Computing—From Single Chip to Datacenters
Customizable Computing—From Single Chip to Datacenters
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DOI：
10.1109/jproc.2018.2876372
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发表时间：
2019
影响因子：
20.6
通讯作者：
J. Cong;Zhenman Fang;Muhuan Huang;Peng Wei;Di Wu;Cody Hao Yu
中科院分区：
文献类型：
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作者：
J. Cong;Zhenman Fang;Muhuan Huang;Peng Wei;Di Wu;Cody Hao Yu
自2009年成立以来，特定领域计算中心（CDSC）一直专注于可定制计算。我们相信，未来的计算系统将可通过加速器的广泛使用进行定制，因为定制设计的加速器通常提供比通用处理器高10- 100倍的性能/能效。这种加速器丰富的架构呈现出与经典冯诺依曼架构的根本背离，该架构强调在公共流水线上有效共享不同指令的执行，从而在计算资源稀缺时提供优雅的解决方案。相比之下，富含加速器的架构具有异构性和可定制性，以提高能源效率;这更适合于硅资源丰富且有利于空间计算的能源受限设计-随着Dennard扩展的结束，情况就是如此。目前，可定制计算已经引起了极大的兴趣;例如，英特尔在2015年以170亿美元收购Altera以及亚马逊在其AWS公共云中引入现场可编程门阵列（FPGA）就是明证。在本文中，我们提出了CDSC的研究计划和成就的概述可定制计算，从单芯片到服务器节点和嵌入式中心，广泛使用组合加速器和FPGA。我们强调了我们在几个应用领域的成功，如医学成像，机器学习和计算基因组学。除了架构创新之外，一个同样重要的研究维度可以实现定制计算的自动化。这包括自动化编译，用于将源代码级转换与高效的参数化架构模板生成相结合，以及高效的运行时支持，用于调度和透明的资源管理，用于集成FPGA以实现企业级加速，并支持现有的编程接口，如MapReduce，Hadoop和Spark，用于大规模分布式计算。我们将介绍这些领域的最新进展，并讨论未来的挑战和机遇。
Since its establishment in 2009, the Center for Domain-Specific Computing (CDSC) has focused on customizable computing. We believe that future computing systems will be customizable with extensive use of accelerators, as custom-designed accelerators often provide 10–100X performance/energy efficiency over the general-purpose processors. Such an accelerator-rich architecture presents a fundamental departure from the classical von Neumann architecture, which emphasizes efficient sharing of the executions of different instructions on a common pipeline, providing an elegant solution when the computing resource is scarce. In contrast, the accelerator-rich architecture features heterogeneity and customization for energy efficiency; this is better suited for energy-constrained designs where the silicon resource is abundant and spatial computing is favored—which has been the case with the end of Dennard scaling. Currently, customizable computing has garnered great interest; for example, this is evident by Intel’s $17 billion acquisition of Altera in 2015 and Amazon’s introduction of field-programmable gate-arrays (FPGAs) in its AWS public cloud. In this paper, we present an overview of the research programs and accomplishments of CDSC on customizable computing, from single chip to server node and to datacenters, with extensive use of composable accelerators and FPGAs. We highlight our successes in several application domains, such as medical imaging, machine learning, and computational genomics. In addition to architecture innovations, an equally important research dimension enables automation for customized computing. This includes automated compilation for combining source-code-level transformation for high-level synthesis with efficient parameterized architecture template generations, and efficient runtime support for scheduling and transparent resource management for integration of FPGAs for datacenter-scale acceleration with support to the existing programming interfaces, such as MapReduce, Hadoop, and Spark, for large-scale distributed computation. We will present the latest progress in these areas, and also discuss the challenges and opportunities ahead.