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DTSTART;TZID=America/New_York:20221102T120000
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DTSTAMP:20260629T145737
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LAST-MODIFIED:20221103T213443Z
UID:5946-1667390400-1667394000@ece.northeastern.edu
SUMMARY:Yuexi Zhang's PhD Proposal Review
DESCRIPTION:“Human Body and Activity Analysis” \nAbstract: \nHuman-related applications such as person detection\, human pose estimations and human activity recognition\, that always draw a lot of attentions in computer vision community. In this proposal\, we discuss several related topics that we are interested in\, and demonstrate how we improve the existing methods. The first problem we consider is video-based human pose estimation. For most general approaches\, researchers focus on collecting human poses from each frame independently and then associate them based on matching or tracking methods. However\, such the pipeline usually relies on complex computations and also consumes running time. To overcome such shortages\, we propose a light weighted network with the unsupervised training strategy\, that aims to reduce running time but remaining the performance. The next problem we explore is about cross-view action recognition (CVAR). The goal of CVAR is to recognize a human action when observed from a previously unseen viewpoint. This is important for some applications such as surveillance systems where is not practical or feasible to collect large amounts of training data when adding a new camera. In this case\, it requires methods that are able to generate reliable view-invariant information trained with given viewpoints and recognize the action from an unseen viewpoint. In general\, most approaches rely on 3D data\, but using 2D data is still under-discovered. Besides\, the performance of those approaches using only 2D data is far worse than 3D approaches. Therefore\, we propose a simple yet efficient CVAR framework that takes 2D data as input and close the performance gap between 3D and 2D input. The last problem we investigate is online action detection and we are interested in detecting action start at current stage. Online action start detection problem is to detect an action startpoint as soon as it occurs with its action category in untrimmed\, streaming videos\, and it has potential applications such as early alert generation in surveillance systems. The typical approaches usually heavily rely on frame-level annotations and also they are limited to pre-defined action categories. Therefore\, we propose a novel yet simple design\, 3D MLP-mxier based architecture that aims to detect the taxonomy-free action start without using frame-level annotations. \n  \nCommittee: \nDr. Octavia Camps(Advisor) \nDr. Mario Sznaier \nDr. Sarah Ostadabbas
URL:https://ece.northeastern.edu/event/yuexi-zhangs-phd-proposal-review/
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DTSTART;TZID=America/New_York:20221102T140000
DTEND;TZID=America/New_York:20221102T150000
DTSTAMP:20260629T145737
CREATED:20221103T213638Z
LAST-MODIFIED:20221103T213638Z
UID:5950-1667397600-1667401200@ece.northeastern.edu
SUMMARY:Kai Huang's PhD Dissertation Defense
DESCRIPTION:“Partitioning Data Across Multiple\, Network Connected FPGAs with High Bandwidth Memory to Accelerate Non-streaming Applications” \nAbstract:\nField Programmable Gate Arrays (FPGAs) are increasingly used in cloud computing to increase the run time of various applications. Flexibility\, efficiency and lower power enable FPGAs to be important components in modern data centers. Applications such as Secure Function Evaluation (SFE)\, graph processing\, and machine learning are increasingly mapped to FPGA-based adaptable cloud computing platforms. However\, due to resource limitations\, it is difficult to map applications to only one FPGA. Applications with a streaming data processing pattern can be mapped to a multiple-FPGA platform where the FPGAs are connected in a 1-D or ring topology\, thus communications overhead can be pipelined with computations. The communication\, merely passing data from boards to boards\, will not significantly affect the system performance if the bandwidth is sufficient. In a more general processing pattern involving non-streaming applications\, each FPGA is responsible for only a portion of the computation and the FPGAs must keep exchanging data during the run time of the application. The communication cost can be the bottleneck of such a system. The challenge is how to map and parallelize these applications to a multi-FPGA cloud computing platform in such a way that communication is minimized and speedup is maximized.\nIn this research\, we build a framework to map garbled circuit applications\, an implementation of SFE\, to a cloud computing platform that has FPGA cards attached to computing nodes. The FPGAs on the node are able to communicate directly through the network. The framework consists of two parts: hardware design and software preprocessing. The hardware design integrates with the Xilinx UDP network stack enabling the capability to exchange data through the network and thus bypassing the processor and its software stack. The framework also takes advantage of High Bandwidth Memory (HBM) for high off-chip memory throughput. The levels of memory hierarchy available on the FPGA are used for caching both local data and incoming and outgoing network data. Preprocessing will generate the reordered batches of each layer needed for processing\, efficient memory allocation and final memory layout. We also applied an effective partitioning algorithm to schedule executions to different FPGAs to minimize the communication between FPGAs. By generating different size of problems from the EMP-toolkit\, we can demonstrate that this hardware-software co-design framework achieves nearly optimal two times speedup on a two-FPGA setup compared to a one-FPGA implementation. We explore extremely large examples that cannot be mapped to one-FPGA\, proving that it is achievable to map large examples of billions of operations to this distributed heterogeneous system. \nCommittee: \nProf. Miriam Leeser(advisor) \nProf. Stratis Ioannidis(co-advisor) \nProf. Mieczyslaw Kokar
URL:https://ece.northeastern.edu/event/kai-huangs-phd-dissertation-defense/
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