Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time=2021-10-15 8:40
|time='''2026-04-10 10:30'''
|addr=Main Building B1-612
|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=Low Power Wide Area Networks (LPWAN) such as Long Range (LoRa) show great potential in emerging aquatic IoT applications. However, our deployment experience shows that the floating LPWAN suffer significant performance degradation, compared to the static terrestrial deployments. Our measurement results reveal the reason behind this is due to the polarization and directivity of the antenna. The dynamic attitude of a floating node incurs varying signal strength losses, which is ignored by the attitude-oblivious link model adopted in most of the existing methods. When accessing the channel at a misaligned attitude, packet errors can happen. In this paper, we propose an attitude-aware link model that explicitly quantifies the impact of node attitude on link quality. Based on the new model, we propose PolarTracker, a novel channel access method for floating LPWAN. PolarTracker tracks the node attitude alignment state and schedules the transmissions into the aligned periods with better link quality. We implement a prototype of PolarTracker on commercial LoRa platforms and extensively evaluate its performance in various real-world environments. The experimental results show that PolarTracker can efficiently improve the packet reception ratio by 48.8%, compared with ALOHA in LoRaWAN.
|abstract = To effectively utilize heterogeneous specialized hardware units in modern GPUs, such as TensorCores and Tensor Memory Accelerators, this paper introduces PipeThreader, a new DNN compiler. PipeThreader proposes shifting scheduling functionality from hardware to software so as to enable more efficient and sophisticated computation pipelining with minimal manual effort. This is achieved through sTask-graph, a new DNN computation abstraction, a hierarchical hardware abstraction that captures the capabilities of specialized units, and new scheduling primitives. As a result, PipeThreader can discover efficient pipeline scheduling for well-studied DNN architectures like FlashAttention, achieving comparable or even superior performance. Additionally, it can uncover novel pipeline schemes for emerging models like Mamba2, delivering significantly better performance compared to state-of-the-art hand-crafted implementations. The code is open-sourced at https://github.com/tile-ai/tilelang.
|confname=INFOCOM 2021
|confname =OSDI'25
|link=https://ieeexplore.ieee.org/document/9488714
|link = https://www.usenix.org/conference/osdi25/presentation/cheng
|title=PolarTracker: Attitude-aware Channel Access for Floating Low Power Wide Area Networks
|title= PipeThreader: Software-defined pipelining for efficient DNN execution
|speaker=Wenliang
|speaker=Junzhe
}}
|date=2026-4-9
{{Latest_seminar
|abstract=Due to the limited computing capacity in mobile devices, device-to-device (D2D) computation offloading has been proposed as a promising solution to improving the quality of service in the Internet of things (IoT) networks, by allowing mobile devices to exploit spare computing resources in nearby user devices. However, a major challenge to realizing this new paradigm is how to effectively motivate user devices to participate as computation providers (CPs) for computation requesters (CRs), which is further exacerbated by the fact that user incentives are usually coupled with information asymmetry between the network operator and user devices. This has not been sufficiently studied for D2D computation offloading. In this paper, we propose a signaling-based incentive mechanism that leverages contract theory to address information asymmetry for D2D computation offloading. Based on the proposed contract-based incentive mechanism, we also solve the many-to-many CP-CR pairing problem by devising a polynomial-complexity matching scheme. Simulation results show that our proposed algorithm can effectively motivate user devices to participate in D2D computation offloading and select the most appropriate CPs to perform the computation tasks for corresponding CRs.
|confname=IoTJ 2021
|link=https://ieeexplore.ieee.org/abstract/document/9523573
|title=Signaling-based Incentive Mechanism for D2D Computation Offloading
|speaker=Wenjie
}}
}}


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

Latest revision as of 10:37, 10 April 2026

Time: 2026-04-10 10:30
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [OSDI'25] PipeThreader: Software-defined pipelining for efficient DNN execution, Junzhe
    Abstract: To effectively utilize heterogeneous specialized hardware units in modern GPUs, such as TensorCores and Tensor Memory Accelerators, this paper introduces PipeThreader, a new DNN compiler. PipeThreader proposes shifting scheduling functionality from hardware to software so as to enable more efficient and sophisticated computation pipelining with minimal manual effort. This is achieved through sTask-graph, a new DNN computation abstraction, a hierarchical hardware abstraction that captures the capabilities of specialized units, and new scheduling primitives. As a result, PipeThreader can discover efficient pipeline scheduling for well-studied DNN architectures like FlashAttention, achieving comparable or even superior performance. Additionally, it can uncover novel pipeline schemes for emerging models like Mamba2, delivering significantly better performance compared to state-of-the-art hand-crafted implementations. The code is open-sourced at https://github.com/tile-ai/tilelang.

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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