Intelligent Computing Memory Systems for Data-Intensive Applications

适用于数据密集型应用的智能计算内存系统

• 批准号: 552042-2020
• 负责人: Fang, Zhenman
• 金额: $ 8.74万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=702997
• 关键词: Intelligent; Computing; Memory; Systems; Data

In the big data era, for many data-intensive applications, such as machine learning, video processing, computational genomics, big data analytics, and network processing, their performance and energy efficiency are limited by not only the computation itself, but also the data communication. As a result, traditional general-purpose and computing-centric platforms can no longer sustain the ever-increasing demand. In this proposal, we aim to design the next-generation computing platform for data-intensive applications, with the partnership of Huawei Canada in its business of intelligent computing (https://e.huawei.com/ca/solutions/hic). Canada has a long history of innovation in the architecture design and tool automation for fine-grained reconfigurable architectures. By incorporating both specialized hardware acceleration and data-centric computing, we propose to develop an intelligent computing memory system, where we will design a high performance and energy efficient coarse-grained reconfigurable hardware accelerator chip and integrate it onto both the processor and memory sides. Moreover, we propose to provide full system support, so that data-intensive applications can easily and efficiently utilize the entire system and process the data where they sit, including the conventional processor, processor-side and memory-side accelerators, to optimize the computation and minimize the data movement. Our proposed research will develop novel hardware architecture, critical technologies and reusable methodology, as well as corresponding compilation and modeling tools, for next-generation intelligent computing memory systems in the big data era. The majority of the research results will be open source to benefit a broader community in Canada. Our program will also train next-generation highly qualified professionals and prepare their career in the highly-demanded information and communication technology sector (https://www.ic.gc.ca/eic/site/ict-tic.nsf/eng/h_it07229.html). All these will keep Canada internationally competitive in computing technologies that are key to the economy growth and healthcare improvement.

在大数据时代，对于机器学习、视频处理、计算基因组学、大数据分析、网络处理等许多数据密集型应用，其性能和能效不仅受到计算本身的限制，也受到数据通信的限制。因此，传统的通用和以计算为中心的平台不再能够满足日益增长的需求。 在该提案中，我们的目标是与华为加拿大公司在其智能计算业务(https://e.huawei.com/ca/solutions/hic).)方面建立合作伙伴关系，为数据密集型应用设计下一代计算平台加拿大在细粒度可重构架构的架构设计和工具自动化方面有着悠久的创新历史。通过将专门的硬件加速和以数据为中心的计算相结合，我们提出了开发一个智能计算存储系统，在这个系统中，我们将设计一个高性能、高能效的粗粒度可重构硬件加速器芯片，并将其集成到处理器和存储器端。此外，我们建议提供全面的系统支持，使数据密集型应用程序可以轻松高效地利用整个系统，并在它们所在的位置处理数据，包括传统的处理器、处理器端和内存端加速器，以优化计算并最大限度地减少数据移动。 我们提出的研究将为大数据时代的下一代智能计算存储系统开发新的硬件体系结构、关键技术和可重用的方法，以及相应的编译和建模工具。大多数研究成果将是开源的，以造福于加拿大更广泛的社区。我们的计划还将培养下一代高素质的专业人员，并为他们在高要求的信息和通信技术部门(https://www.ic.gc.ca/eic/site/ict-tic.nsf/eng/h_it07229.html).的职业生涯做准备所有这些都将保持加拿大在计算技术方面的国际竞争力，计算技术是经济增长和医疗保健改善的关键。