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Grant: Alliance Grants

Simon Fraser University

$120,000.00

Intelligent Computing Memory Systems for Data-Intensive Applications

FY 2022-23

Summary
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.
Program
Alliance Grants
Location
British Columbia, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
552042-2020
Name nom
Fang, Zhenman Z
Department département
Engineering Science, School of
Organization id
5
Institution établissement
Simon Fraser University
Province en
British Columbia
Province fr
Colombie-Britannique
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2020
Award amount
120000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1207
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Information, computer and communication technologies
Area of application fr
Technologie de l'information, des ordinateurs et des réseaux de communications
Research subject code
2702
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Computer systems organization
Research subject fr
Organisation des systèmes informatiques
Installment
inst:2
Partie
1
Nb partie
2
Application title
Intelligent Computing Memory Systems for Data-Intensive Applications
Application summary
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.