TechTalk#3- Cerebras Systems: A Systems Approach to Deep Learning

We are excited to invite all of you again to attend the third TECHNICAL TALK, TECH-TALK, in the INDUSTRY EXPOSURE SERIES.
We are hosting this event with Cerebras, https://cerebras.net/
Information about INTERNSHIP & FULL-TIME EMPLOYMENT OPPORTUNITIES
at the company will follow the event.
We will be giving out one of amazon(Canada) or UberEats(Canada) GIFT-CARDS (CAD 15 each) to the attending students who register and attend the event for more than 30 minutes.
Please find more information about the event below.
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EVENT INFORMATION
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DATE        : Thursday, Jun 10, 2021
TIME        : 11 AM – 12 PM
REG. LINK   : https://utoronto.zoom.us/meeting/register/tZUpfuCopjMqG91xCU3cZByrcHqHfwCc0yP2
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TITLE:
Cerebras Systems: A Systems Approach to Deep Learning
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ABSTRACT:
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The rise of Machine Learning workloads has spurred new interest in specialized hardware architectures, which offer significant improvements in performance and power efficiency. However, a substantial challenge faced by these architectures is providing a highly productive software interface, which also unlocks the hardware’s full performance.
In this talk, we will provide an overview of the software stack we’re developing at Cerebras to program the Wafer Scale Engine (WSE) 2. The WSE2 is the largest chip ever built, with 850,000 AI-optimized cores, and orders of magnitude more compute, memory, and interconnect bandwidth than single die offerings. We’ll focus on some of the challenging and interesting problems, which we’re solving to make the WSE2’s massive performance accessible and easy to use. We will also cover Machine Learning on the Wafer-Scale Engine.
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SPEAKER BIO.:
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KEVIN MURRAY
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Kevin E. Murray is a Member of Technical Staff at Cerebras Systems in Toronto. He received his PhD in Electrical and Computer Engineering from the University of Toronto in 2020. He was previously the lead developer of the Verilog to Routing (VTR) project, a visiting Research Assistant at Imperial College London, and has worked on digital design flows at Advanced Micro Devices (AMD). His research interests include Computer Aided Design (CAD) algorithms, and architecture for Machine Learning accelerators and FPGAs.
NATALIA VASSILIEVA
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Natalia Vassilieva is the Director of Product at Cerebras. Her focus is machine learning and artificial intelligence, analytics, and application-driven software-hardware optimization and co-design. Previously, Natalia has been a Sr. Research Manager at Hewlett Packard Labs, where she led the Software and AI group and served as the head of HP Labs Russia from 2011 till 2015. Prior to HPE, she was an Associate Professor at St. Petersburg State University and worked as a software engineer for different IT companies. Natalia holds a PhD in computer science from St. Petersburg State University.
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More Company Info.:
Info on 2nd gen product line here: https://www.anandtech.com/show/16626/cerebras-unveils-wafer-scale-engine-two-wse2-26-trillion-transistors-100-yield
Collaboration with AstraZeneca: https://larslynnehansen.medium.com/accelerating-drug-discovery-research-with-new-ai-models-a-look-at-the-astrazeneca-cerebras-b72664d8783
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Cheers
ECEGSS

Trivia Night on June 3rd – join us!

Hi ECE grads!

The ECEGSS is hosting a virtual trivia night on June 3 at 6 pm. Come alone or with a group of friends to test your skills! All participants will receive a $15 dollar Amazon or Uber Eats gift card and the team that takes home the top prize will receive an additional $15 dollars each!

Register for the event below:
https://utoronto.zoom.us/meeting/register/tZwuc-CsqTsrGNZcoRBnYYFXXrjtpo_3Iac9

See you on Thursday!

ECEGSS exec team

Bi-weekly running/biking/walking event

Hello Hello ?

We are thrilled to share an exciting event. ECEGSS is hosting a bi-weekly running/biking/walking event to PROMOTE health and wellness. All you have to do is participate and achieve some easily achievable goals to win one of the many $20 Amazon Gift Cards!

Targets in a period of two weeks (May 27th – June 12th)

2 hours of biking
1 hour of running and or
4 hours of walking.


To claim your gift(s), we suggest using a fitness app, preferably Strava, or any fitness tracker, such as Fitbit, Apple Watch, Garmin, etc. We will be collecting data to modify our targets and gifts in the coming events, so it is not only to justify our claim for the gifts but also to keep track of your progress.

You‘ll be responsible for making sure you provide authentic information as requested in the following link: https://forms.gle/hibWYSxP8uJUi88t9

Cheers
ECEGSS
https://www.instagram.com/ecegssofficial/
https://twitter.com/UofTECEGSS

ECEGSS TechTalk – Groq

We are excited to invite all of you again to attend the second TECHNICAL TALK, TECH-TALK, in the INDUSTRY EXPOSURE SERIES.

We are hosting the second event with GROQ on May 13, 202, 11 AM – 12 PM.
The presenters will also do a short DEMO of their product.
Information about FULL-TIME EMPLOYMENT OPPORTUNITIES at the company will follow the event.

We will be giving out one of Amazon (Canada) or UberEats (Canada) GIFT-CARDS (CAD 15 each) to the attending students who register and attend the event for more than 30 minutes. Please find more information about the event below.
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EVENT INFORMATION
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DATE        : Thursday, May 13, 2021
TIME        : 11 AM – 12 PM
REG. LINK   : https://utoronto.zoom.us/meeting/register/tZcvcu-tqDooG9XkKsWL08UGOKdVZ4LZy3ai
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AGENDA:
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11:00-11:20 – Andrew Ling, GM and SW Director, presents on Groq and Path to Win AI Space
11:20-11:35 – Groq Demo by Mark Wong-VanHaren, Visualizer and Scheduler viewer, and presents roles within his organization
11:35-11:40 – Jennifer Hwang presents roles and type of people she’s looking for in the HW organization
11:40-11:50 – Q&A
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TITLE:
Think Fast!  A simplistic approach to tackling machine learning and compute at the end of Moore’s Law.
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ABSTRACT:
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Recently closing a $300M funding round, one of the largest funding rounds in AI chip history, Groq has invented an entirely new approach to solve deep learning workloads and HPC.  Based on dataflow and streaming concepts, the Groq Tensor Streaming Processor is able to achieve a 4x improvement compared to other model GPUs and accelerators.
The TSP is built based on two key observations: (1) machine learning workloads exhibit abundant data parallelism, which can be readily mapped to tensors in hardware, and (2) a deterministic processor with a stream programming model enables precise reasoning and control of hardware components to achieve good performance and power efficiency. The TSP is designed to exploit parallelism inherent in machine-learning workloads including instruction-level, memory concurrency, data and model parallelism. This guarantees determinism by eliminating all reactive elements in the hardware, for example, arbiters and caches. Early ResNet50 image classification results demonstrate 20.4K processed images per second with a batch size of one.
Our first ASIC implementation of the TSP architecture yields a computational density of more than 1 TOp/s per square mm of silicon. This TSP is a 25x29mm 14nm chip operating at a nominal clock frequency of 900MHz. The TSP also demonstrates a novel hardware-software approach to achieve fast yet predictable performance on machine-learning workloads within a desired power envelope. This architecture can be deployed across a broad range of applications, from ML to HPC within the datacenter, where low-latency and high-throughput are critical facets of total cost of ownership.
In this talk, we will explore the Groq TSP and help you understand how its simplistic approach to compute yields significant efficiency for acceleration.
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Speaker Bio.:
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ANDREW LING : General Manager and SW Director
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Andrew Ling received his Ph.D. from the University of Toronto in 2009.  After that, he spent most of his time at Altera, which was later acquired by Intel.  His primary focus at Altera/Intel was the development of the OpenCL compiler and later on, moved into CNN acceleration for FPGAs. He now leads the Groq Canada Design Centre, where they are focused on developing scalable ML software and compiler tools for the Groq TSP.
JENNIFER HWANG :: Director, Hardware Engineering
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Jen is an engineer at heart. She has spent her entire career building ASICs, with over 30 successful tape-outs under her belt. Starting at MIT, Jennifer graduated with her Masters and Bachelors in Electrical Engineering. No stranger to startups, she was a part of Silicon Spice startup, which was acquired by Broadcom. Jen became a Director of Engineering responsible for Broadcom’s cellular processor division for hardware verification with a multi-site global team. At Groq, she is leading the Hardware verification team creating ASICs for machine learning. Specifically, she loves to roll up her sleeves and up to her elbows in the details of how things work. In her free time, she supports her family ? reading, skiing, and serving as a parent-volunteer for her two growing kids at school.
MARK WONG-VANHAREN :: Technical Lead, Groq Tools
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Mark has 30 years experience in software development. He co-founded Excite.com, was CTO of ecommerce site Glyde, and served as VP Ramp;D at Wordnik. Over the years, he’s designed and built a wide range of tools and products, including: a special-purpose search engine, a columnar DBMS, a category-best iPhone app, and a templating language and compiler. At Groq, Mark works on our compiler as well as developer tools. He especially enjoys taking complex problems and mercilessly simplifying, usually using a functional programming language. Mark studied computational linguistics at Stanford and maintains a strong interest in natural and programming languages. Outside of work, Mark enjoys tandem biking and creating music with his family band.
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More Company Info.:
https://www.forbes.com/sites/amyfeldman/2021/04/14/ai-chip-startup-groq-founded-by-ex-googlers-raises-300-million-to-power-autonomous-vehicles-and-data-centers/?sh=2000d63c65e3
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Regards
ECEGSS
https://www.instagram.com/ecegssofficial/
https://twitter.com/UofTECEGSS

Science Rendezvous UofT 2021 Virtual Event – May 8, 2021 @UofTSR

We would like to announce that Science Rendezvous 2021 will be taking place VIRTUALLY on Saturday, May 8, 2021, from 10am to 4pm! Each year, the Science Rendezvous street fair attracts tens of thousands of visitors to the University of Toronto, aiming to foster an appreciation of S.T.E.A.M. (science, technology, engineering, art, and mathematics) in the community and to enhance scientific literacy. We will continue to run our programming this year through an online format and would like to invite you to attend our events – from Science Chase experiments, our annual Science Fair competition, and our first ever production, “The SR Show”, featuring engaging demonstrations from over 30 University of Toronto departments. We hope you will join us for a day filled with fun and science! 

Next Steps for the Session:

  • Visit our SR 2021 website for more information on all of the events offered on May 8, 2021
  • Register for the event via the Eventbrite RSVP Form. After registering, you will receive an email with more information and a Zoom meeting link
  • Ensure you have downloaded Zoom 

***Note: the Zoom Link will be active within 24-hours before the session. We will send you an email with the Zoom link prior to the event***

If you have any questions, please do not hesitate to reach out to us directly at uoftsr.mediamarketing@gmail.com. We hope to see you at the upcoming SR 2021 event virtually! 

Thank you,

Miranda Li, Emily Fung, Julia Turner, Noshin Chowdhury, & Sophia Feng

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Promotions Coordinator 2021
Science Rendezvous 
University of Toronto, St. George

Website: sciencerendezvousuoft.ca
Facebook: https://www.facebook.com/UofTSR
Instagram: https://www.instagram.com/uoftsr/
Twitter: https://twitter.com/UofTSR