Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2025-04-11 10:30-12:00'''
|time='''2025-05-23 10:30-12:00'''
|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]].
Line 18: Line 18:
|abstract = Deploying deep convolutional neural networks (CNNs) for edge-based video analytics poses significant challenges due to the intensive computing demands. Model partitioning has emerged as a promising solution by offloading segments of CNNs to multiple proximal edge devices for collaborative inference. However, this approach often incurs substantial cross-device transmission overhead, particularly in handling intermediate feature maps. To address these limitations, we propose ReDream (REsidual feature-DRivEn mixed spArse coding for Model partitioning), a novel edge-centric video analytics framework that jointly optimizes  transmission efficiency and inference accuracy. ReDream introduces two key innovations: 1) It enhances the sparsity of intermediate features by replacing activation functions with ReLU in selected CNN layers and retraining, thereby increasing the proportion of zero-valued elements. 2) It leverages the heterogeneous distribution of feature data across layers by applying a mixed sparse coding scheme, i.e., selecting different compression methods adaptively to optimize model partitioning. These optimizations enable ReDream to support more efficient cross-device inference while maintaining high model accuracy, making it well-suited for real-time deployment in collaborative edge environments.
|abstract = Deploying deep convolutional neural networks (CNNs) for edge-based video analytics poses significant challenges due to the intensive computing demands. Model partitioning has emerged as a promising solution by offloading segments of CNNs to multiple proximal edge devices for collaborative inference. However, this approach often incurs substantial cross-device transmission overhead, particularly in handling intermediate feature maps. To address these limitations, we propose ReDream (REsidual feature-DRivEn mixed spArse coding for Model partitioning), a novel edge-centric video analytics framework that jointly optimizes  transmission efficiency and inference accuracy. ReDream introduces two key innovations: 1) It enhances the sparsity of intermediate features by replacing activation functions with ReLU in selected CNN layers and retraining, thereby increasing the proportion of zero-valued elements. 2) It leverages the heterogeneous distribution of feature data across layers by applying a mixed sparse coding scheme, i.e., selecting different compression methods adaptively to optimize model partitioning. These optimizations enable ReDream to support more efficient cross-device inference while maintaining high model accuracy, making it well-suited for real-time deployment in collaborative edge environments.
|confname = IDEA
|confname = IDEA
|link = https://mobinets.cn/site/Resource:Seminar
|link = https://mns.uestc.cn/wiki/Research:InProgress/MixedSparseCoding
|title= ReDream: Residual Feature-Driven Mixed Sparse Coding for Model Partitioning
|title= ReDream: Residual Feature-Driven Mixed Sparse Coding for Model Partitioning
|speaker=Xianyang
|speaker=Xianyang

Revision as of 20:22, 22 May 2025

Time: 2025-05-23 10:30-12:00
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [SenSys'24] FDLoRa: Tackling Downlink-Uplink Asymmetry with Full-duplex LoRa Gateways, Chenkai
    Abstract: Unlike traditional data collection applications (e.g., environment monitoring) that are dominated by uplink transmissions, the newly emerging applications (e.g., device actuation, firmware update, packet reception acknowledgement) also pose ever-increasing demands on downlink transmission capabilities. However, current LoRaWAN falls short in supporting such applications primarily due to downlink-uplink asymmetry. While the uplink can concurrently receive multiple packets, downlink transmission is limited to a single logical channel at a time, which fundamentally hinders the deployment of downlink-hungry applications. To tackle this practical challenge, FDLoRa develops the first-of-its-kind in-band full-duplex LoRa gateway design with novel solutions to mitigate the impact of self-interference (i.e., strong downlink interference to ultra-weak uplink reception), which unleashes the full spectrum for in-band downlink transmissions without compromising the reception of weak uplink packets. Built upon the full-duplex gateways, FDLoRa introduces a new downlink framework to support concurrent downlink transmissions over multiple logical channels of available gateways. Evaluation results demonstrate that FDLoRa boosts downlink capacity by 5.7x compared to LoRaWAN on a three-gateway testbed and achieves 2.58x higher downlink concurrency per gateway than the state-of-the-art.
  2. [IDEA] ReDream: Residual Feature-Driven Mixed Sparse Coding for Model Partitioning, Xianyang
    Abstract: Deploying deep convolutional neural networks (CNNs) for edge-based video analytics poses significant challenges due to the intensive computing demands. Model partitioning has emerged as a promising solution by offloading segments of CNNs to multiple proximal edge devices for collaborative inference. However, this approach often incurs substantial cross-device transmission overhead, particularly in handling intermediate feature maps. To address these limitations, we propose ReDream (REsidual feature-DRivEn mixed spArse coding for Model partitioning), a novel edge-centric video analytics framework that jointly optimizes transmission efficiency and inference accuracy. ReDream introduces two key innovations: 1) It enhances the sparsity of intermediate features by replacing activation functions with ReLU in selected CNN layers and retraining, thereby increasing the proportion of zero-valued elements. 2) It leverages the heterogeneous distribution of feature data across layers by applying a mixed sparse coding scheme, i.e., selecting different compression methods adaptively to optimize model partitioning. These optimizations enable ReDream to support more efficient cross-device inference while maintaining high model accuracy, making it well-suited for real-time deployment in collaborative edge environments.

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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