IEEE Toronto Virtual AGM


The IEEE Toronto Section is happy to announce our first ever online IEEE Toronto section Annual General Meeting (AGM).  Since we are not restricted to a limited number of physical participants, we are happy to open this even up to all IEEE Toronto members, as well as any guests who they would like to invite.  Please feel free to pass this information along to any interested parties.

We will hear from the IEEE Toronto section, IEEE Canada, and IEEE Global representatives, as well as keynote speakers from local industry.  Awards will be presented to oustanding contributors for the past year, and prizes will be available for all attendees.  You must register for the event using the link on this page in order to qualify for prizes.  Only IEEE members will be eligible for prizes.

  Date and Time




  • Date: 13 Nov 2020
  • Time: 06:00 PM to 08:00 PM
  • All times are Canada/Eastern
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  • Toronto
  • Canada

  • Starts 01 October 2020 09:00 AM
  • Ends 13 November 2020 05:00 PM
  • All times are Canada/Eastern
  • No Admission Charge


Inmar Givoni

Inmar Givoni


AI for Self-Driving Cars

At the Uber ATG R&D centre, we are working on advanced state-of-the-art models for solving a large range of problems in self driving - perception and prediction, motion planning, mapping and localization, sensor simulation, and more. All that work is publicly available through academic conferences and venues. In this talk I will cover some exciting recent advances and also discuss the path to production - how we go from research prototypes to deployed systems on vehicle.


Inmar Givoni is a Director of Engineering at Uber Advanced Technology Group, Toronto, where she leads a team whose mission is to bring from research and into production cutting-edge deep-learning models for self-driving vehicles. She received her PhD (Computer Science) in 2011 from the University of Toronto, specializing in machine learning, and was a visiting scholar at the University of Cambridge. She worked at Microsoft Research, Altera (now Intel), Kobo, and Kindred at roles ranging from research scientist to VP, Big Data, applying machine learning techniques to various problem domains and taking concepts from research to production systems. She is an inventor of several patents and has authored numerous top-tier academic publications in the areas of machine learning, computer vision, and computational biology. She is a regular speaker at AI events, and is particularly interested in outreach activities for young women, encouraging them to choose technical career paths. For her volunteering efforts she has received the 2017 Arbor Award from UofT. In 2018 she was recognized as one of Canada’s 50 inspiring women in STEM and recently recognized as one of Canada’s Tech Titans: Top 19 of 2019.. She was featured in Marie ClaireToronto LifeThe Globe and Mail,  TWIML & AI podcastReWork’s list of 30 influential women in Canadian AIUofT’s News, and other media venues.

Address:Toronto, Canada

Martin Snelgrove

Martin Snelgrove


Building Cool Silicon in the Frozen North

Back in the ‘80s, ambitious Canadian Si engineers took their expensive taxpayer-funded educations down to Santa Clara, where the action (and money) was. Now a lot of them and the best of them stay here, making the action (and the money). Telecom and graphics got that started, FPGAs kept up the momentum, and all that built a good solid engineering base; and now AI is red hot, pulling all the big players up here. And Canadians are developing their own management style that beats the hierarchical British and transactional American styles: it turns out that nice guys finish first in team sports.
So to the hardware side of our AI opportunity: computer architecture is seeing its first real change since von Neumann sketched it out in the ‘40s. When you look at the early days of computing, whether mainframe or microprocessor, you see that things we see as obvious — binary representation, fixed-point, algorithm, code — being explained in painstaking detail. We’re at that stage now with AI, where we need to get used to geometry in thousands of dimensions, to knowledge expressed as probability, to pattern replacing algorithm, to massively distributed systems. And at the same time we have to deal with power consumption as the central focus of design instead of an after-the-fact nuisance, since having chip power density that rivals the surface of the sun is putting us on the wrong foot with physics.
Untether is neck-deep in all of this, and the changes are so profound that there’s room for us all on the AI tundra.


Martin is CTO of Untether AI, who have just announced their first product: a high-performance AI chip that puts Peta-operations per second onto a board. The magic to getting the massive computing power AI needs is to be very careful with the femtoJoules: you can only fit so many watts in a box, so you have to use them very carefully. It turns out that to do that you have to rethink John von Neumann’s 1947 computer architecture, and it turns out that understanding AI as a workload lets you do that. 
Martin was a professor at the University of Toronto, then had a Nortel/Mitel-supported industrial research chair at Carleton. Over 16 years of teaching he saw the vast majority of the students Canada paid for head straight down to California. So he moved over to the dark side, and has been in the founding team for three tech companies in Toronto; Soma, Kapik and now Untether. It turns out that because Canada produces great engineers, you can give them a great place to work by putting teams together. Top-grade talent likes working with top-grade talent.


6:00pm: Introduction and online meeting details 

6:05pm: Section Chair report from Ali Nabavi

6:15pm: IEEE Global update from Kathy Land

6:30pm: Prize draw

6:35pm: Keynote presentation from Dr. Inmar Givoni, Director of Engineering at Uber Advanced Technology Group, Toronto

7:05pm: IEEE Canada update from Maike Luiken

7:20pm: Keynote presentation from Dr. Martin Snelgrove, CTO at Untether AI

7:50pm: Awards presentation and prize draw