Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time=2021-07-06 9:30
|time='''2025-01-10 10:30-12:00'''
|addr=Main Building B1-705 (PhD only)
|addr=4th Research Building A518
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
|note=Useful links: [[Resource:Reading_List|📚 Readling list]]; [[Resource:Seminar_schedules|📆 Schedules]]; [[Resource:Previous_Seminars|🧐 Previous seminars]].
}}
}}


===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=Works in 2021 circle
|abstract = Recently, smart roadside infrastructure (SRI) has demonstrated the potential of achieving fully autonomous driving systems. To explore the potential of infrastructure-assisted autonomous driving, this paper presents the design and deployment of Soar, the first end-to-end SRI system specifically designed to support autonomous driving systems. Soar consists of both software and hardware components carefully designed to overcome various system and physical challenges. Soar can leverage the existing operational infrastructure like street lampposts for a lower barrier of adoption. Soar adopts a new communication architecture that comprises a bi-directional multi-hop I2I network and a downlink I2V broadcast service, which are designed based on off-the-shelf 802.11ac interfaces in an integrated manner. Soar also features a hierarchical DL task management framework to achieve desirable load balancing among nodes and enable them to collaborate efficiently to run multiple data-intensive autonomous driving applications. We deployed a total of 18 Soar nodes on existing lampposts on campus, which have been operational for over two years. Our real-world evaluation shows that Soar can support a diverse set of autonomous driving applications and achieve desirable real-time performance and high communication reliability. Our findings and experiences in this work offer key insights into the development and deployment of next-generation smart roadside infrastructure and autonomous driving systems.
|confname=PhD forum
|confname =MobiCom'24
|link=https://mobinets.cn/site/index.php?title=Resource:Seminar
|link = https://dl.acm.org/doi/abs/10.1145/3636534.3649352
|title= Research work progressing
|title= Soar: Design and Deployment of A Smart Roadside Infrastructure System for Autonomous Driving
|speaker=All
|speaker=Jiahao
|date=2025-01-10
}}{{Latest_seminar
|abstract = GPUs are increasingly utilized for running DNN tasks on emerging mobile edge devices. Beyond accelerating single task inference, their value is also particularly apparent in efficiently executing multiple DNN tasks, which often have strict latency requirements in applications. Preemption is the main technology to ensure multitasking timeliness, but mobile edges primarily offer two priorities for task queues, and existing methods thus achieve only coarse-grained preemption by categorizing DNNs into real-time and best-effort, permitting a real-time task to preempt best-effort ones. However, the efficacy diminishes significantly when other real-time tasks run concurrently, but this is already common in mobile edge applications. Due to different hardware characteristics, solutions from other platforms are unsuitable. For instance, GPUs on traditional mobile devices primarily assist CPU processing and lack special preemption support, mainly following FIFO in GPU scheduling. Clouds handle concurrent task execution, but focus on allocating one or more GPUs per complex model, whereas on mobile edges, DNNs mainly vie for one GPU. This paper introduces Pantheon, designed to offer fine-grained preemption, enabling real-time tasks to preempt each other and best-effort tasks. Our key observation is that the two-tier GPU stream priorities, while underexplored, are sufficient. Efficient preemption can be realized through software design by innovative scheduling and novel exploitation of the nested redundancy principle for DNN models. Evaluation on a diverse set of DNNs shows substantial improvements in deadline miss rate and accuracy of Pantheon over state-of-the-art methods.
|confname =MobiSys'24
|link = https://dl.acm.org/doi/abs/10.1145/3643832.3661878
|title= Pantheon: Preemptible Multi-DNN Inference on Mobile Edge GPUs
|speaker=Jiale
|date=2025-01-10
}}
}}


=== History ===
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 10:37, 10 January 2025

Time: 2025-01-10 10:30-12:00
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [MobiCom'24] Soar: Design and Deployment of A Smart Roadside Infrastructure System for Autonomous Driving, Jiahao
    Abstract: Recently, smart roadside infrastructure (SRI) has demonstrated the potential of achieving fully autonomous driving systems. To explore the potential of infrastructure-assisted autonomous driving, this paper presents the design and deployment of Soar, the first end-to-end SRI system specifically designed to support autonomous driving systems. Soar consists of both software and hardware components carefully designed to overcome various system and physical challenges. Soar can leverage the existing operational infrastructure like street lampposts for a lower barrier of adoption. Soar adopts a new communication architecture that comprises a bi-directional multi-hop I2I network and a downlink I2V broadcast service, which are designed based on off-the-shelf 802.11ac interfaces in an integrated manner. Soar also features a hierarchical DL task management framework to achieve desirable load balancing among nodes and enable them to collaborate efficiently to run multiple data-intensive autonomous driving applications. We deployed a total of 18 Soar nodes on existing lampposts on campus, which have been operational for over two years. Our real-world evaluation shows that Soar can support a diverse set of autonomous driving applications and achieve desirable real-time performance and high communication reliability. Our findings and experiences in this work offer key insights into the development and deployment of next-generation smart roadside infrastructure and autonomous driving systems.
  2. [MobiSys'24] Pantheon: Preemptible Multi-DNN Inference on Mobile Edge GPUs, Jiale
    Abstract: GPUs are increasingly utilized for running DNN tasks on emerging mobile edge devices. Beyond accelerating single task inference, their value is also particularly apparent in efficiently executing multiple DNN tasks, which often have strict latency requirements in applications. Preemption is the main technology to ensure multitasking timeliness, but mobile edges primarily offer two priorities for task queues, and existing methods thus achieve only coarse-grained preemption by categorizing DNNs into real-time and best-effort, permitting a real-time task to preempt best-effort ones. However, the efficacy diminishes significantly when other real-time tasks run concurrently, but this is already common in mobile edge applications. Due to different hardware characteristics, solutions from other platforms are unsuitable. For instance, GPUs on traditional mobile devices primarily assist CPU processing and lack special preemption support, mainly following FIFO in GPU scheduling. Clouds handle concurrent task execution, but focus on allocating one or more GPUs per complex model, whereas on mobile edges, DNNs mainly vie for one GPU. This paper introduces Pantheon, designed to offer fine-grained preemption, enabling real-time tasks to preempt each other and best-effort tasks. Our key observation is that the two-tier GPU stream priorities, while underexplored, are sufficient. Efficient preemption can be realized through software design by innovative scheduling and novel exploitation of the nested redundancy principle for DNN models. Evaluation on a diverse set of DNNs shows substantial improvements in deadline miss rate and accuracy of Pantheon over state-of-the-art methods.

History

2024

2023

2022

2021

2020

  • [Topic] [ The path planning algorithm for multiple mobile edge servers in EdgeGO], Rong Cong, 2020-11-18

2019

2018

2017

Instructions

请使用Latest_seminar和Hist_seminar模板更新本页信息.

    • 修改时间和地点信息
    • 将当前latest seminar部分的code复制到这个页面
    • 将{{Latest_seminar... 修改为 {{Hist_seminar...,并增加对应的日期信息|date=
    • 填入latest seminar各字段信息
    • link请务必不要留空,如果没有link则填本页地址 https://mobinets.org/index.php?title=Resource:Seminar
  • 格式说明
    • Latest_seminar:

{{Latest_seminar
|confname=
|link=
|title=
|speaker=
}}

    • Hist_seminar

{{Hist_seminar
|confname=
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|date=
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