Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2024-09-20 10:30-12:00'''
|time='''2025-09-19 10:30'''
|addr=4th Research Building A518
|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]].
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{{Latest_seminar
{{Latest_seminar
|abstract = Overlapping cameras offer exciting opportunities to view a scene from different angles, allowing for more advanced, comprehensive and robust analysis. However, existing video analytics systems for multi-camera streams are mostly limited to (i) per-camera processing and aggregation and (ii) workload-agnostic centralized processing architectures. In this paper, we present Argus, a distributed video analytics system with cross-camera collaboration on smart cameras. We identify multi-camera, multi-target tracking as the primary task of multi-camera video analytics and develop a novel technique that avoids redundant, processing-heavy identification tasks by leveraging object-wise spatio-temporal association in the overlapping fields of view across multiple cameras. We further develop a set of techniques to perform these operations across distributed cameras without cloud support at low latency by (i) dynamically ordering the camera and object inspection sequence and (ii) flexibly distributing the workload across smart cameras, taking into account network transmission and heterogeneous computational capacities. Evaluation of three real-world overlapping camera datasets with two Nvidia Jetson devices shows that Argus reduces the number of object identifications and end-to-end latency by up to 7.13× and 2.19× (4.86× and 1.60× compared to the state-of-the-art), while achieving comparable tracking quality.
|abstract = With cloud-side computing and rendering, mobile cloud gaming (MCG) is expected to deliver high-quality gaming experiences to budget mobile devices. However, our measurement on representative MCG platforms reveals that even under good network conditions, all platforms exhibit high interactive latency of 112–403 ms, from a user-input action to its display response, that critically affects users’ quality of experience. Moreover, jitters in network latency often lead to significant fluctuations in interactive latency. In this work, we collaborate with a commercial MCG platform to conduct the first in-depth analysis on the interactive latency of cloud gaming. We identify VSync, the synchronization primitive of Android graphics pipeline, to be a key contributor to the excessive interactive latency; as many as five VSync events are intricately invoked, which serialize the complex graphics processing logic on both the client and cloud sides. To address this, we design an end-to-end VSync regulator, dubbed LoopTailor, which minimizes VSync events by decoupling game rendering from the lengthy cloud-side graphics pipeline and coordinating cloud game rendering directly with the client. We implement LoopTailor on the collaborated platform and commodity Android devices, reducing the interactive latency (by ∼34%) to stably below 100 ms.
|confname=TMC' 24
|confname =NSDI'25
|link = https://ieeexplore.ieee.org/abstract/document/10682605
|link = https://www.usenix.org/conference/nsdi25/presentation/li-yang
|title= Argus: Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart Cameras
|title= Dissecting and Streamlining the Interactive Loop of Mobile Cloud Gaming
|speaker=Bairong
|speaker= Li Chen
|date=2024-9-29
|date=2025-9-9
}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract = We present FarfetchFusion, a fully mobile live 3D telepresence system. Enabling mobile live telepresence is a challenging problem as it requires i) realistic reconstruction of the user and ii) high responsiveness for immersive experience. We first thoroughly analyze the live 3D telepresence pipeline and identify three critical challenges: i) 3D data streaming latency and compression complexity, ii) computational complexity of volumetric fusion-based 3D reconstruction, and iii) inconsistent reconstruction quality due to sparsity of mobile 3D sensors. To tackle the challenges, we propose a disentangled fusion approach, which separates invariant regions and dynamically changing regions with our low-complexity spatio-temporal alignment technique, topology anchoring. We then design and implement an end-to-end system, which achieves realistic reconstruction quality comparable to existing server-based solutions while meeting the real-time performance requirements (<100 ms end-to-end latency, 30 fps throughput, <16 ms motion-to-photon latency) solely relying on mobile computation capability.
|abstract = The local deployment of large language models (LLMs) on mobile devices has garnered increasing attention due to its advantages in enhancing user privacy and enabling offline operation. However, given the limited computational resources of a single mobile device, only small language models (SLMs) with restricted capabilities can currently be supported. In this paper, we explore the potential of leveraging the collective computing power of multiple mobile devices to collaboratively support more efficient local LLM inference. We evaluate the feasibility and efficiency of existing parallelism techniques under the constraints of mobile devices and wireless network, identifying that chunked pipeline parallelism holds promise for realizing this vision. Building on this insight, we propose FlexSpark, a novel solution designed to achieve efficient and robust multi-device collaborative inference. FlexSpark incorporates priority scheduling, ordered communication, and elastic compression to maximize wireless bandwidth utilization, and thus accelerates distributed inference. Preliminary experimental results demonstrate that FlexSpark achieves up to a 2 × speedup compared to state-of-the-art frameworks, significantly enhancing the practicality and scalability of LLM deployment on mobile devices.
|confname=MobiCom' 23
|confname =APNet'25
|link = https://dl.acm.org/doi/abs/10.1145/3570361.3592525
|link = https://dl.acm.org/doi/10.1145/3735358.3735368
|title= FarfetchFusion: Towards Fully Mobile Live 3D Telepresence Platform
|title= FlexSpark: Robust and Efficient Multi-Device Collaborative Inference over Wireless Network
|speaker=Mengfan
|speaker=Ruizhen
|date=2024-9-29
|date=2025-9-19
}}
}}
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 18:03, 18 September 2025

Time: 2025-09-19 10:30
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [NSDI'25] Dissecting and Streamlining the Interactive Loop of Mobile Cloud Gaming, Li Chen
    Abstract: With cloud-side computing and rendering, mobile cloud gaming (MCG) is expected to deliver high-quality gaming experiences to budget mobile devices. However, our measurement on representative MCG platforms reveals that even under good network conditions, all platforms exhibit high interactive latency of 112–403 ms, from a user-input action to its display response, that critically affects users’ quality of experience. Moreover, jitters in network latency often lead to significant fluctuations in interactive latency. In this work, we collaborate with a commercial MCG platform to conduct the first in-depth analysis on the interactive latency of cloud gaming. We identify VSync, the synchronization primitive of Android graphics pipeline, to be a key contributor to the excessive interactive latency; as many as five VSync events are intricately invoked, which serialize the complex graphics processing logic on both the client and cloud sides. To address this, we design an end-to-end VSync regulator, dubbed LoopTailor, which minimizes VSync events by decoupling game rendering from the lengthy cloud-side graphics pipeline and coordinating cloud game rendering directly with the client. We implement LoopTailor on the collaborated platform and commodity Android devices, reducing the interactive latency (by ∼34%) to stably below 100 ms.
  2. [APNet'25] FlexSpark: Robust and Efficient Multi-Device Collaborative Inference over Wireless Network, Ruizhen
    Abstract: The local deployment of large language models (LLMs) on mobile devices has garnered increasing attention due to its advantages in enhancing user privacy and enabling offline operation. However, given the limited computational resources of a single mobile device, only small language models (SLMs) with restricted capabilities can currently be supported. In this paper, we explore the potential of leveraging the collective computing power of multiple mobile devices to collaboratively support more efficient local LLM inference. We evaluate the feasibility and efficiency of existing parallelism techniques under the constraints of mobile devices and wireless network, identifying that chunked pipeline parallelism holds promise for realizing this vision. Building on this insight, we propose FlexSpark, a novel solution designed to achieve efficient and robust multi-device collaborative inference. FlexSpark incorporates priority scheduling, ordered communication, and elastic compression to maximize wireless bandwidth utilization, and thus accelerates distributed inference. Preliminary experimental results demonstrate that FlexSpark achieves up to a 2 × speedup compared to state-of-the-art frameworks, significantly enhancing the practicality and scalability of LLM deployment on mobile devices.

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

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