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X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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TZID:Asia/Hong_Kong
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DTSTART;TZID=Asia/Hong_Kong:20260514T093000
DTEND;TZID=Asia/Hong_Kong:20260514T103000
DTSTAMP:20260514T071847
CREATED:20260512T043316Z
LAST-MODIFIED:20260512T082808Z
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SUMMARY:Seminar on Terahertz Needle Beam Forming Transceiver Systems Based on Advanced Packaging and Massive Chiplet Integration
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/98399262138 \nAbstract\nFuture sensing and communication applications are converging toward a common technical objective: the development of miniaturized\, integrated\, reconfigurable\, high-speed\, and high-precision wireless hardware. These specific requirements fundamentally push the frequency of wireless systems into sub-THz regime\, where both large absolute bandwidth and electrically large apertures can be achieved within a compact physical size. In this talk\, three hardware solutions are presented\, targeting sub-THz needle beam forming transceiver systems based on advanced packaging and massive chiplet integration. First\, a 140-GHz monostatic SoC transceiver is demonstrated\, which features a low-inherent-loss full-duplexer with self-interference cancellation\, effectively mitigating beam-misalignment issues when interfacing with large THz EM apertures. Second\, to explore the feasibility of >200GHz passive antennas and interconnects within low-cost organic packages\, another 240-GHz SiP transceiver tailored for cross-polarimetric radar sensing is developed. Finally\, implementing the 240-GHz SiP transceiver as the spatial feed\, a 240-GHz 6400-element aperiodic reflectarray consists of 25 organic package modules and 400 CMOS chiplet integration is presented\, which saves 93% of the silicon area with a given radiating aperture. The full system demonstrates <0.9° beamwidth with ±50° 2D beam steering range\, and high-resolution near-field imaging. These hardware solutions and prototyping considerations provide a practical pathway toward large-scale\, distributed and reconfigurable sub-THz wireless hardware. \nSpeaker\nDr. Xibi CHEN\nDoctor of Philosophy\,\nMassachusetts Institute of Technology (MIT)\, Cambridge\, MA \nSpeaker’s Biography\nXibi CHEN received his Ph.D. degree from the Department of Electrical Engineering and Computer Science (EECS)\, Massachusetts Institute of Technology (MIT)\, Cambridge\, MA\, USA\, in 2026. He received his B.S. and M.S. degrees from Tsinghua University\, Beijing\, China\, in 2017 and 2020\, respectively. From 2015 to 2017\, he was a Research Assistant with the Microwave and Antenna Institute\, Department of Electronic Engineering\, Tsinghua University. He later became a Graduate Student Researcher in the same institute from 2017 to 2020. From 2020 to 2026\, he was a Graduate Student Researcher at EECS\, MIT. His research background includes terahertz (THz) integrated circuits and systems\, electromagnetics\, advanced packaging technologies\, large-scale phased arrays\, radar sensing\, and high-speed communications. He worked in Texas Instruments (Kilby Lab) and Intel Corporation as Summer Research Interns in 2023 and 2024\, respectively. Dr. Chen was the recipient of 2025-2026 IEEE SSCS Predoctoral Achievement Award\, and 2024 IEEE MTT-S Tom Brazil Graduate Fellowship. He also received 2025 MIT EECS MathWorks Fellowship Award\, ISSCC 2022 Student Travel Grant Award\, and Analog Devices Outstanding Student Designer Award. \nOrganiser\nProf. Kaibin HUANG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong
URL:https://ece.hku.hk/events/20260514-1/
CATEGORIES:Highlights,Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260514T103000
DTEND;TZID=Asia/Hong_Kong:20260514T113000
DTSTAMP:20260514T071847
CREATED:20260508T020416Z
LAST-MODIFIED:20260508T020416Z
UID:115879-1778754600-1778758200@ece.hku.hk
SUMMARY:RPG Seminar – HIMSA: A Heterogeneous In-Memory Computing and Searching Architecture for Efficient Attention-Based Models
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/99174148480?pwd=duVxaYZOJDT6MWxDh4OKOMmyo12A7A.1 \nAbstract\nThe Transformer architecture\, the foundation for modern large language models (LLMs)\, has revolutionized natural language processing and other AI domains. However\, its significant computational and memory requirements\, primarily from the matrix multiplication in the self-attention mechanism\, present major challenges for conventional hardware. While intensive research on in-memory computing (IMC) technology offers a path to overcome the memory bottleneck\, using IMC for Transformers remains challenging. This is because the dynamic matrix multiplication with frequently changing Key\, Query\, and Value matrices require frequent and costly write operations that are ill-suited for non-volatile memories (NVM) technologies like ReRAM. This work introduces HIMSA Heterogeneous In-Memory Computing and Searching Architecture\, which employs vector quantization on K and V matrices. This technique transforms the dynamic vector-matrix multiplications into static operations performed on pre-trained codebooks\, thereby eliminating the need for runtime write operations in the attention mechanism. The proposed architecture was evaluated through circuit-level simulations that account for the peripheral circuit designs\, including nearest neighbor search and the division-less Softmax operations. More importantly\, its write-free attention mechanism mitigates the concerns over limited write endurance of ReRAM devices . This work presents a promising pathway toward highly efficient NVM-based hardware acceleration for next-generation AI models. \nSpeaker\nMr Muyuan PENG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nPeng Muyuan received the B.S. degree at the University of Science and Technology of China majored in Applied Physics. He is currently pursuing the Ph.D. degree in electrical and electronic engineering at the Department of Electrical and Electronic Engineering\, The University of Hong Kong. His current research interests include non-volatile memory devices\, in-memory computing and related neural network accelerations. \nOrganiser\nProfessor Can LI \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260514-2/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260514T133000
DTEND;TZID=Asia/Hong_Kong:20260514T143000
DTSTAMP:20260514T071847
CREATED:20260505T021413Z
LAST-MODIFIED:20260505T021413Z
UID:115830-1778765400-1778769000@ece.hku.hk
SUMMARY:RPG Seminar – Cooperative Edge AI: From Event-triggered Inference to Efficient Model Downloading
DESCRIPTION:Zoom Link \nhttp://hku.zoom.us/j/7074144117?omn=95813783034 \nAbstract\nCooperative edge AI enables edge devices and edge servers to collaboratively execute intelligent tasks under limited computation\, storage\, energy\, and communication resources. In this talk\, we discuss two complementary research directions toward communication-efficient cooperative edge AI. First\, we introduce an event-triggered cooperative inference framework for rare-event detection in edge intelligence systems. Rare events are usually infrequent but highly critical\, while conventional edge inference systems may overlook them due to data imbalance and rigid resource allocation. To address this issue\, a dual-threshold multi-exit architecture is adopted\, allowing confident normal events to be processed locally while complex or uncertain rare events are selectively offloaded to the edge server for more accurate classification. Second\, we present an efficient AI model downloading framework based on parametric-sensitivity-aware retransmission. Instead of treating all model parameters equally\, this framework exploits the unequal importance of neural network parameters and allocates wireless retransmission resources to more sensitive model packets. In this way\, downloading latency can be reduced while inference performance is preserved. The talk concludes with a discussion of future research directions in cooperative edge AI\, highlighting open challenges and opportunities in communication-efficient inference\, adaptive model deployment\, and resource-aware edge intelligence. \nSpeaker\nMr Zhou You\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nZhou You is currently pursuing a Ph.D. degree in the Department of Electrical and Electronic Engineering at The University of Hong Kong\, under the supervision of Prof. Kaibin Huang. He received his B.Eng. degree in Electrical Engineering from the University of Wisconsin–Madison\, USA\, in 2021. His research interests include wireless communications\, edge inference\, and AI model downloading. \nOrganiser\nProf. Kaibin HUANG \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260514/
CATEGORIES:Seminar
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