BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系 - ECPv6.16.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
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
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Hong_Kong
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20230101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240516T110000
DTEND;TZID=Asia/Hong_Kong:20240516T120000
DTSTAMP:20260512T175330
CREATED:20240507T081518Z
LAST-MODIFIED:20250114T063319Z
UID:18499-1715857200-1715860800@ece.hku.hk
SUMMARY:RPG Seminar – Domain-Specific Efficient Neural Network Architecture Design
DESCRIPTION:Abstract\nAI models significantly impact our daily lives\, but their high performance brings the challenge of model complexity. Deploying these models on edge devices poses additional challenges\, including power consumption\, memory storage and latency constraints. In this seminar\, we will delve into designing efficient neural network architectures for various domains\, including low-level computer vision and neural fields. We will start by discussing the latest Lookup Table (LUT)-based approach for Single-Image Super-Resolution (SISR). Our proposed Hundred-Kilobyte LUT (HKLUT) requires only 100KB\, 10X less than the second smallest LUT-based method\, and delivers superior performance. Moreover\, we will explore the field of Implicit Neural Representation (INR)\, where inference efficiency is often overlooked. We propose the Activation-Sharing Multi-Resolution (ASMR) coordinate network to enhance INR’s rendering efficiency. By sharing activations across data grids\, ASMR can reduce its Multiply-Accumulate (MAC) operations by up to 500x and improve reconstruction quality. \nSpeaker\nMr. Jason Chun Lok LI\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker\nMr. Jason Chun Lok LI holds a BEng degree from the Department of Electrical and Electronic Engineering at The University of Hong Kong\, obtained in 2020. He is currently continuing his studies at the same institution\, working towards a PhD. His research interest lies in the development of domain-specific techniques for efficient deep learning on edge devices. \nOrganizer\nProf. Ngai WONG \nAll are welcome.
URL:https://ece.hku.hk/events/20240516-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240516T143000
DTEND;TZID=Asia/Hong_Kong:20240516T153000
DTSTAMP:20260512T175330
CREATED:20240507T082448Z
LAST-MODIFIED:20250114T063241Z
UID:18500-1715869800-1715873400@ece.hku.hk
SUMMARY:RPG Seminar – Exploration of Novel Operators with Memristor Arrays Towards Efficient and Robust In-memory Computing
DESCRIPTION:Abstract\nThe past decade of escalated development in deep learning (DL) has achieved unprecedented success in engineering fields. In particular\, deep neural networks (DNNs) via deep learning have achieved remarkable success across various applications. However\, challenges remain in the hardware implementation of these software-oriented AI algorithms\, primarily due to the reliance on traditional von Neumann computing architectures which are inefficient and lead to high power usage and latency particularly at the edge computing level. To address these issues\, compute-in-memory (CIM) using non-volatile memristive devices presents a promising solution. CIM leverages in-memory data processing to reduce data movement\, thereby improving efficiency. Therefore\, a core issue in artificial intelligence-related fields lies in leveraging hardware practice experience to explore and develop neuron models and operational operators. In the upcoming talk\, an innovative memristive unit cell based on the arithmetic unit model will be introduced\, aiming to explore its performance and robustness in emerging operational networks within AI fields. \nSpeaker\nMr. Yuan REN\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker\nMr. Yuan REN received the M.S. degree in electrical and computer engineering from the University of Macau (UM)\, Macao. He then joined the SoC Key Laboratory\, Peking University Shenzhen Institute and PKU-HKUST Shenzhen-Hong Kong Institution\, Guangdong\, China. He is currently pursuing the Ph.D. degree in electrical and electronic engineering from the University of Hong Kong (HKU)\, under the supervision of Dr. Ngai Wong. His research focuses on algorithm-hardware co-design for AI accelerator and memristor-based compute-in-memory integrated circuits. \nOrganizer\nProf. Ngai WONG \nAll are welcome.
URL:https://ece.hku.hk/events/20240516-2/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
END:VCALENDAR