Demystifying the Characteristics of High Bandwidth Memory for Real-Time Systems

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hdl:2117/362010
Document typeConference report
Defense date2021-12
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
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ProjectSuPerCom - Sustainable Performance for High-Performance Embedded Computing Systems (EC-H2020-772773)
Mont-Blanc 2020 - Mont-Blanc 2020, European scalable, modular and power efficient HPC processor (EC-H2020-779877)
MASTECS - Multicore Analysis Service and Tools for Embedded Critical Systems (EC-H2020-878752)
Mont-Blanc 2020 - Mont-Blanc 2020, European scalable, modular and power efficient HPC processor (EC-H2020-779877)
MASTECS - Multicore Analysis Service and Tools for Embedded Critical Systems (EC-H2020-878752)
Abstract
The number of functionalities controlled by software on every critical real-time product is on the rise in domains like automotive, avionics and space. To implement these advanced functionalities, software applications increasingly adopt artificial intelligence algorithms that manage massive amounts of data transmitted from various sensors. This translates into unprecedented memory performance requirements in critical systems that the commonly used DRAM memories struggle to provide. High-Bandwidth Memory (HBM) can satisfy these requirements offering high bandwidth, low power and high-integration capacity features. However, it remains unclear whether the predictability and isolation properties of HBM are compatible with the requirements of critical embedded systems. In this work, we perform to our knowledge the first timing analysis of HBM. We show the unique structural and timing characteristics of HBM with respect to DRAM memories and how they can be exploited for better time predictability, with emphasis on increased isolation among tasks and reduced worst-case memory latency.
CitationAsifuzzaman, K. [et al.]. Demystifying the Characteristics of High Bandwidth Memory for Real-Time Systems. A: IEEE International Conference on Computer-Aided Design. "2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD): 1-4 Nov. 2021, Munich, Germany: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 1-9. ISBN 978-1-6654-4507-8. DOI 10.1109/ICCAD51958.2021.9643473.
ISBN978-1-6654-4507-8
ISSN1558-2434
Publisher versionhttps://ieeexplore.ieee.org/document/9643473
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