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X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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BEGIN:VTIMEZONE
TZID:Asia/Hong_Kong
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TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260514T103000
DTEND;TZID=Asia/Hong_Kong:20260514T113000
DTSTAMP:20260515T055143
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260514T133000
DTEND;TZID=Asia/Hong_Kong:20260514T143000
DTSTAMP:20260515T055143
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260515T140000
DTEND;TZID=Asia/Hong_Kong:20260515T150000
DTSTAMP:20260515T055143
CREATED:20260512T081414Z
LAST-MODIFIED:20260512T081414Z
UID:115938-1778853600-1778857200@ece.hku.hk
SUMMARY:RPG Seminar – Stabilizing Streaming Video Geometry via Dynamic Feature Normalization
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/94833409754?pwd=ER6VaveQbdEzOQzhuFKThpSdRusUDs.1 \nAbstract\nConsistent 3D geometry estimation from streaming RGB input is crucial for real-world applications such as autonomous driving\, embodied AI\, and large-scale reconstruction. \nWhile modern monocular geometry foundation models achieve strong single-image accuracy\, they exhibit severe temporal inconsistency on continuous input\, notably dominated by scale–shift drifting. Through targeted empirical analysis\, we trace this instability to its root cause: fluctuations in latent feature statistics\, whose mean and variance directly determine the predicted depth’s scale and shift. Building on this insight\, we introduce Dynamic Feature Normalization (DyFN)\, a lightweight\, causal recurrent module that dynamically and robustly modulates feature statistics to maintain stable geometry over time. We adapt powerful pretrained monocular geometry models for streaming by finetuning only DyFN\, a mere 2% additional parameters\, while keeping the backbone frozen\, thereby achieving temporal consistency without compromising single-image accuracy. Extensive experiments across four benchmarks show that DyFN effectively eliminates temporal artifacts such as disjointed layering and positional jitter\, and achieves state-of-the-art temporal stability\, improving over prior streaming methods by up to 14% and even outperforming heavier non-causal video baselines. \nSpeaker\nMr Xiaoyang LYU\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nXiaoyang Lyu is a fourth-year PhD student in the CVMI Lab at the University of Hong Kong\, where he is supervised by Prof. Xiaojuan Qi. He holds a Master’s degree from Zhejiang University and a Bachelor’s degree from the Harbin Institute of Technology.\nXiaoyang’s research focuses on bridging the gap between physical and digital environments by replicating complex physics\, geometry\, and material properties within simulators. He is driven by the conviction that high-fidelity world modeling is essential for advancing embodied AI and developing agents that can effectively assist in the real world. \nOrganiser\nProf Xiaojuan QI \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260515-2/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260515T160000
DTEND;TZID=Asia/Hong_Kong:20260515T170000
DTSTAMP:20260515T055143
CREATED:20260511T020048Z
LAST-MODIFIED:20260511T020048Z
UID:115888-1778860800-1778864400@ece.hku.hk
SUMMARY:RPG Seminar – Unveiling the Relationship Between Cation Content and Zeta Potential of Colloids for Forming High-Quality Perovskites
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/94017530661 \nAbstract\nThere are numerous studies focusing on the crystallization dynamics of perovskite materials. However\, the change of precursor properties which can also significantly affect crystallization behavior\, is always ignored. In this seminar\, we establish a comprehensive understanding of the relationship between A-site cations content and zeta potential of precursor\, revealing its influence on perovskite formation and crystallization dynamics. Through in-situ photoluminescence (PL) and X-ray diffraction (XRD) analyses\, we demonstrate how zeta potential impacts the formation process and crystallization behavior of perovskites. Furthermore\, we explore the effects of zeta potential on the optical and electrical properties of the resulting materials. Our findings indicate that achieving a zeta potential near zero facilitates the fabrication of high-quality and additive-free perovskites\, leading to enhanced performance in perovskite solar cells (PSCs) and perovskite light-emitting diodes (PeLEDs). This work provides vital insights into tuning interfacial properties for improved perovskite optoelectronic devices. \nSpeaker\nMr. Qi XIONG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nQi Xiong received the B.S. degree in Polymer Materials and Engineering from Hainan University\, and the M.S. degree in Material Science and Engineering from South China University of Technology. He is currently pursuing the Ph.D. degree in the Department of Electrical and Computer Engineering\, Faculty of Engineering\, The University of Hong Kong. His current research interests include perovskite synthesis and blue perovskite light-emitting diodes (PeLEDs). \nOrganiser\nProf. Wallace C.H. CHOY \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260515/
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260518T160000
DTEND;TZID=Asia/Hong_Kong:20260518T170000
DTSTAMP:20260515T055143
CREATED:20260513T040826Z
LAST-MODIFIED:20260513T062829Z
UID:115975-1779120000-1779123600@ece.hku.hk
SUMMARY:RPG Seminar – Robust Multivariate Autoregressive Model Estimation under Impulsive Noise
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/9995553636?omn=96185323037 \nAbstract\nThis seminar presents robust multivariate autoregressive (MVAR) model estimation under impulsive noise\, with a focus on extending bias-compensated instrumental-variable methods to contaminated multichannel observations. MVAR models are widely used in time-series prediction\, system identification\, biomedical signal analysis\, and sensor-array processing. However\, conventional estimators can suffer from systematic bias under correlated measurement noise\, and their performance can degrade severely when rare but large-amplitude impulsive samples dominate covariance and correlation statistics. \nThe talk first reviews the transition from AR to MVAR modeling and explains how measurement noise enters the estimation problem. It then introduces the extended instrumental-variable bias-compensation (EIV-BC) framework for correlated noise and discusses why impulsive contamination presents an additional challenge. A robust batch EIV-BC strategy is presented\, using bidirectional estimation and residual-based outlier exclusion to identify unreliable samples before recomputing model statistics. \nExperiments are conducted on both simulated MVAR signals and real-world UCI air-quality sensor data. The results show that impulsive noise significantly degrades standard EIV-BC prediction\, while the robust extension provides more stable estimates and lower prediction errors on non-impulsive test samples. The seminar demonstrates how robust sample selection can improve MVAR estimation reliability in practical noisy sensing environments. \nSpeaker\nMr Mingxi LYU\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nMingxi Lyu received the B.S. degree in Mechanical Engineering from Xi’an Jiao tong University in 2019\, and the M.S. degree in Mechanical Engineering from Xi’an Jiao tong University in 2022. 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 multivariate autoregressive regression and statistical robust estimation of chirp signal with their applications. \nOrganiser\nProf. Shing Chow CHAN \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260518/
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260519T160000
DTEND;TZID=Asia/Hong_Kong:20260519T170000
DTSTAMP:20260515T055143
CREATED:20260420T064633Z
LAST-MODIFIED:20260420T064633Z
UID:115720-1779206400-1779210000@ece.hku.hk
SUMMARY:RPG Seminar – From Understanding to Intervention: Interpretability-Guided Methods for Improving Large Language Models
DESCRIPTION:  \nAbstract\nLarge language models have achieved impressive performance\, but improving them efficiently and reliably requires more than scaling alone. In this talk\, I present a series of works that explore how internal understanding of LLMs can be translated into practical interventions for better capability\, efficiency\, and controllability. I begin with actionable mechanistic interpretability\, introducing a unified “Locate\, Steer\, and Improve” perspective that turns model analysis into a framework for intervention. I then show how this perspective supports several concrete advances: data-free mixed-precision quantization guided by numerical and structural sensitivity\, multilingual capability enhancement through representation shifting and contrastive alignment\, personalized multi-teacher distillation that routes each prompt to its most suitable teacher\, and coarse-to-fine selective fine-tuning for mitigating catastrophic forgetting while preserving general versatility. Together\, these works reflect a common theme: interpretability is not only a tool for explaining LLMs\, but also a principled basis for designing more efficient training\, compression\, and adaptation methods. \nSpeaker\nMr Hengyuan ZHANG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nHengyuan Zhang is a Ph.D. candidate at the University of Hong Kong\, supervised by Prof. Ngai Wong and Prof. Hayden Kwok-Hay So. His research focuses on the improvement of efficiency and interpretability within large language models. He aims to uncover and characterize the internal processes that govern model behavior\, with the goal of improving model speciality\, interpretability\, and reliability in real-world deployments. He has published multiple papers in leading venues such as ACL\, EMNLP\, TKDD\, and NeurIPS. \nOrganiser\nProf. Ngai WONG \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260519/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260520T110000
DTEND;TZID=Asia/Hong_Kong:20260520T120000
DTSTAMP:20260515T055143
CREATED:20260514T081134Z
LAST-MODIFIED:20260514T081134Z
UID:116062-1779274800-1779278400@ece.hku.hk
SUMMARY:RPG Seminar – Foundation-style Methods for Real-Time Statistical Dependency Measurement and Its Applications
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/9021481973?omn=94827890905 \nAbstract\nMutual information has long served as a principled measure of statistical dependence\, but computing it from empirical samples is notoriously difficult: neural estimators rely on costly gradient-based optimization for every new dataset\, which limits their use inside real-time and large-scale pipelines. This talk presents a framework that pretrains a neural estimator on a synthetic meta-distribution and then evaluates any new distribution in a single forward pass — turning a per-dataset optimization problem into an inference problem. Cheap\, online dependency readings further act as many applications\, including live training diagnostics\, and as a regularization signal for detecting key frames. \nSpeaker\nMr. Zhengyang HU\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nZhengyang Hu\, a three-year PhD in ECE\, mainly focuses on statistical dependency measurement and foundational-style data science models. \nOrganiser\nProf Yanchao YANG \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260520/
CATEGORIES:Seminar
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