BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Northeastern University College of Engineering - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://coe.northeastern.edu
X-WR-CALDESC:Events for Northeastern University College of Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220214T100000
DTEND;TZID=America/New_York:20220214T110000
DTSTAMP:20260426T081043
CREATED:20220209T203147Z
LAST-MODIFIED:20220209T203147Z
UID:30226-1644832800-1644836400@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Mengshu Sun
DESCRIPTION:PhD Proposal Review: Deep Learning Acceleration on Edge Devices with Algorithm/System Co-Design \nMengshu Sun \nLocation: Zoom Link \nAbstract: As deep learning has succeeded in a broad range of applications in recent years\, there is an increasing trend towards deploying deep neural networks (DNNs) on edge devices such as FPGAs and mobiles. However\, there exists a significant gap between the extraordinary accuracy of state-of-the-art DNNs and the efficient implementations on edge devices\, due to their limited resources to DNNs with high computation and memory intensity. With the target of simultaneously accelerating the inference and maintaining the accuracy of DNNs\, I investigate efficient implementation of deep learning on low-power and resource-constrained devices in this dissertation\, leveraging algorithm/system co-design techniques that incorporate hardware-friendly DNN compression algorithms with system design optimizations. \nIn the first part of this dissertation\, I explore the DNN compression algorithms leveraging weight pruning and quantization techniques. As for weight pruning\, novel structured and fined-grained sparsity schemes are proposed and obtained with the reweighted regularization pruning algorithm\, and then incorporated into acceleration frameworks on both FPGAs and mobiles to make the acceleration rate of sparse models approach the pruning rate of GFLOPs for the unpruned models. As for quantization\, intra-layer mixed precision/scheme weight quantization is proposed to boost utilization of heterogeneous FPGA resources and therefore improving the FPGA throughput\, by assigning multiple precisions and/or multiple schemes at the filter level within each layer and maintaining the same ratio of filters with different quantization assignments across all the layers. \nIn the second part of this dissertation\, I study the system implementations\, proposing an automatic DNN acceleration framework to generate DNN accelerators to satisfy a target frame rate (FPS). Unlike previous approaches that start from model quantization and then optimizing the FPS for hardware implementations\, this automatic framework will provide an estimation of the FPS with the FPGA resource utilization analysis and performance analysis modules\, and the bit-width is reduced until the target FPS is met and the ratio is automatically determined to guide the quantization process and the accelerator implementation on hardware. A resource utilization model is developed to overcome the difficulty in estimating the LUT consumption\, and a novel computing engine for DNNs is designed with various optimization techniques in support of DNN compression to improve the computation parallelism and resource utilization efficiency.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-mengshu-sun/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220214T120000
DTEND;TZID=America/New_York:20220214T124500
DTSTAMP:20260426T081043
CREATED:20220131T210634Z
LAST-MODIFIED:20220131T210634Z
UID:30064-1644840000-1644842700@coe.northeastern.edu
SUMMARY:Library Webinar: Getting Started with Mendeley
DESCRIPTION:Are you interested in learning how to better organize your PDFs and research sources? Using Mendeley can help you become more organized and efficient throughout the research process: from when you first begin to explore your topic to when you are adding citations to your paper. \nIn this online session\, you will learn how to download Mendeley and set up an account\, organize your research sources and PDFs\, annotate PDFs\, and create in-text citations and bibliographies. (Category: Citation help) \nNOTE: EndNote\, RefWorks\, Zotero\, and Mendeley are similar\, so you only need to choose one. \nThis webinar will be recorded. To receive a copy of the recording\, please register using your Northeastern email address. All the times of the webinars are in EST. \nRegister here: bit.ly/citationmgmtworkshops
URL:https://coe.northeastern.edu/event/library-webinar-getting-started-with-mendeley/
END:VEVENT
END:VCALENDAR