Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2022-6-27 10:30'''
|time='''Friday 10:30-12:00'''
|addr=4th Research Building A527-B
|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]].
}}
}}
Line 7: Line 7:
===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract = Recent advances in network and mobile computing.  
|abstract=Continual learning (CL) trains NN models incrementally from a continuous stream of tasks. To remember previously learned knowledge, prior studies store old samples over a memory hierarchy and replay them when new tasks arrive. Edge devices that adopt CL to preserve data privacy are typically energy-sensitive and thus require high model accuracy while not compromising energy efficiency, i.e., cost-effectiveness. Our work is the first to explore the design space of hierarchical memory replay-based CL to gain insights into achieving cost-effectiveness on edge devices. We present Miro, a novel system runtime that carefully integrates our insights into the CL framework by enabling it to dynamically configure the CL system based on resource states for the best cost-effectiveness. To reach this goal, Miro also performs online profiling on parameters with clear accuracy-energy trade-offs and adapts to optimal values with low overhead. Extensive evaluations show that Miro significantly outperforms baseline systems we build for comparison, consistently achieving higher cost-effectiveness.
|confname=talk
|confname=MobiCom'23
|link=[Resource:Paper Carnival 2022|Paper Carnival 2022
|link=https://arxiv.org/pdf/2308.06053
|title=]
|title=Cost-effective On-device Continual Learning over Memory Hierarchy with Miro
|speaker=all
|speaker=Jiale
 
|date=2024-06-14}}
 
{{Latest_seminar
 
|abstract=Multi-view 3D reconstruction driven augmented, virtual, and mixed reality applications are becoming increasingly edge-native, due to factors such as, rapid reconstruction needs, security/privacy concerns, and lack of connectivity to cloud platforms. Managing edge-native 3D reconstruction, due to edge resource constraints and inherent dynamism of ‘in the wild’ 3D environments, involves striking a balance between conflicting objectives of achieving rapid reconstruction and satisfying minimum quality requirements. In this paper, we take a deeper dive into multi-view 3D reconstruction latency-quality trade-off, with an emphasis on reconstruction of dynamic 3D scenes. We propose data-level and task-level parallelization of 3D reconstruction pipelines, holistic edge system optimizations to reduce reconstruction latency, and long-term minimum reconstruction quality satisfaction. The proposed solutions are validated through collection of real-world 3D scenes with varying degree of dynamism that are used to perform experiments on hardware edge testbed. The results show that our solutions can achieve between 50% to 75% latency reduction without violating long term minimum quality requirements.
}}
|confname=SEC'23
'''Visible Light Communication--Wenliang'''
|link=https://www.cs.hunter.cuny.edu/~sdebroy/publication-files/SEC2023_CR.pdf
 
|title=On Balancing Latency and Quality of Edge-Native Multi-View 3D Reconstruction
[Sensys 2021] [https://dl.acm.org/doi/pdf/10.1145/3485730.3485934 CurveLight: An Accurate and Practical Indoor Positioning System]
|speaker=Yang Wang
 
|date=2024-06-14}}
[Sensys 2021] [https://dl.acm.org/doi/pdf/10.1145/3485730.3485948 SpiderWeb: Enabling Through-Screen Visible Light Communication]
 
'''Lora--Kaiwen'''
 
[ICNP2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9155474 X-MAC: Achieving High Scalability via Imperfect-Orthogonality Aware Scheduling in LPWAN]
 
'''Response to Mobility--Luwei'''
 
[Infocom2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796811 Enabling QoE Support for Interactive Applications over Mobile Edge with High User Mobility]
 
[Infocom2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796968 User Experience Oriented Task Computation for UAV-Assisted MEC System]
 
[TMC2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9343712 ECHO: Efficient Zero-Control-Packet Broadcasting for Mobile Ad Hoc Networks]
 
=== History ===
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 15:22, 11 June 2024

Time: Friday 10:30-12:00
Address: 4th Research Building A518
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [MobiCom'23] Cost-effective On-device Continual Learning over Memory Hierarchy with Miro, Jiale
    Abstract: Continual learning (CL) trains NN models incrementally from a continuous stream of tasks. To remember previously learned knowledge, prior studies store old samples over a memory hierarchy and replay them when new tasks arrive. Edge devices that adopt CL to preserve data privacy are typically energy-sensitive and thus require high model accuracy while not compromising energy efficiency, i.e., cost-effectiveness. Our work is the first to explore the design space of hierarchical memory replay-based CL to gain insights into achieving cost-effectiveness on edge devices. We present Miro, a novel system runtime that carefully integrates our insights into the CL framework by enabling it to dynamically configure the CL system based on resource states for the best cost-effectiveness. To reach this goal, Miro also performs online profiling on parameters with clear accuracy-energy trade-offs and adapts to optimal values with low overhead. Extensive evaluations show that Miro significantly outperforms baseline systems we build for comparison, consistently achieving higher cost-effectiveness.
  2. [SEC'23] On Balancing Latency and Quality of Edge-Native Multi-View 3D Reconstruction, Yang Wang
    Abstract: Multi-view 3D reconstruction driven augmented, virtual, and mixed reality applications are becoming increasingly edge-native, due to factors such as, rapid reconstruction needs, security/privacy concerns, and lack of connectivity to cloud platforms. Managing edge-native 3D reconstruction, due to edge resource constraints and inherent dynamism of ‘in the wild’ 3D environments, involves striking a balance between conflicting objectives of achieving rapid reconstruction and satisfying minimum quality requirements. In this paper, we take a deeper dive into multi-view 3D reconstruction latency-quality trade-off, with an emphasis on reconstruction of dynamic 3D scenes. We propose data-level and task-level parallelization of 3D reconstruction pipelines, holistic edge system optimizations to reduce reconstruction latency, and long-term minimum reconstruction quality satisfaction. The proposed solutions are validated through collection of real-world 3D scenes with varying degree of dynamism that are used to perform experiments on hardware edge testbed. The results show that our solutions can achieve between 50% to 75% latency reduction without violating long term minimum quality requirements.

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

Template loop detected: Resource:Previous Seminars

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