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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:20260514T103000
DTEND;TZID=Asia/Hong_Kong:20260514T113000
DTSTAMP:20260511T035353
CREATED:20260508T020416Z
LAST-MODIFIED:20260508T020416Z
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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:20260511T035353
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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