[Lisa_seminaires] [Tea-Talk] Andreas Moshovos, March 24, 13:45, AA6214

Dzmitry Bahdanau dimabgv at gmail.com
Ven 24 Mar 10:32:00 EDT 2017


Reminder: this is happening today!

*Please put your name in the spreadsheet if you want to speak with Andreas
after the talk.*

Dima

On Fri, 17 Mar 2017 at 15:11 Dzmitry Bahdanau <dimabgv at gmail.com> wrote:

> Hi all,
>
> Our next tea-talk will be given by Dr. Andreas Moshovos *on March 24, at
> 13:45 (new time!), room AA6214*, who is a professor at University of
> Toronto. Hope you see many of you there! *Please let me know if you would
> like to speak to Andreas after the talk. *You can directly put your name
> on the spreadsheet below:
>
>
> https://docs.google.com/spreadsheets/d/1vVn0eOAlTmkmd-DpEwuMD2NEQH_H8NLlY5YVxh1afVE/edit?usp=sharing
>
> *Title: *Exploiting Value Content to Accelerate Inference with
> Convolutional Neural Networks
>
> *Abstract: *Sufficiently capable computing hardware is essential for
> practical applications of Deep Learning. Until very recently computing
> hardware capabilities have been increasing at an exponential rate. As a
> result, around 2010 computing hardware capabilities reached the level
> necessary to demonstrate Deep Learning’s true potential. Unfortunately,
> semiconductor technology scaling, the keep enabler of this past exponential
> growth in capabilities, has slowed down dramatically. Fortunately,
> specialized computing hardware design has the potential to deliver another
> 2 to 3 orders of improvements in computing capabilities.
>
> Our goal is to develop the techniques necessary for boosting computing
> hardware capabilities thus enabling further innovation in Deep Learning.  We
> are developing specialized computing hardware for Deep Learning Networks
> whose key feature is that they are value-based. We have been developing
> value-based accelerators that take advantage of expected properties in the
> runtime calculated value stream of Deep Learning Networks such as the value
> distribution of activations, or even their bit content. Using image
> classification convolutional neural networks, we have demonstrated 2 to 3
> orders of magnitude execution time improvements over conventional graphics
> processor hardware and up to 4.5x improvements over a state-of-the-art
> accelerator. In this talk we will review the need for specialized computing
> hardware for Deep Learning and summarize our efforts. We will also briefly
> touch upon the recently approved NSERC COHESA Strategic Partnership Network
> on Hardware Acceleration for Machine Learning. NSERC COHESA brings together
> 19 Researchers across multiple Canadian Universities and 8 Industrial
> Partners.
>
> *Bio: *Andreas Moshovos teaches how to design and optimize computing
> hardware engines at the University of Toronto where he has the privilege of
> collaborating with several talented students on techniques to improve
> execution time, energy efficiency and cost for computing hardware. He has
> also taught at Northwestern University, USA, the University of Athens,
> Greece, the Hellenic Open University,  Greece, and as an invited
> professor at the École Polytechnique Fédérale de Lausanne, Switzerland. He
> has received the ACM SIGARCH Maurice Wilkes award in 2010, an NSF CAREER
> Award in 2000, two IBM Faculty awards, a Semiconductor Research Corporation
> Inventor recognition award, and a MICRO Hall of Fame award. He has served
> at the Program Chair for the ACM/IEEE International Symposium on
> Microarchiteture and the IEEE International Symposium on the Performance
> Analysis of Systems and Software. He studied computer science at the
> University of Crete, Greece, at New York University, USA, and at the
> University of Wisconsin-Madison, USA.
>
> Best,
> Dima
>
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