Value-Based Deep Learning Hardware Acceleration

A talk by Andreas Moshovos
Professor of Electrical and Computer Engineering, University of Toronto

20 November 2020, 08:30 PM

Have you reserved your ticket?

By submitting your email you agree to the terms
Get tickets

About this talk

I will be reviewing our efforts in identifying value properties of Deep Learning models that hardware accelerators can use to improve execution time performance, memory traffic and storage, and energy efficiency. Our goal it to not sacrifice accuracy and to not require any changes to the model. I will be presenting our accelerator family which includes designs that exploit these properties. Our accelerators exploit ineffectual activations and weights, their variable precision requirements, or even their value content at the bit level. Further, our accelerators also enable on-the-fly trading off accuracy for further performance and energy efficiency improvements.Finally, I will overview NSERC COHESA, a Canadian research network which targets the co-design of next generation machine learning hardware and algorithms.

Have you got yours yet?

Our All-Access Passes are a must if you want to get the most out of this event.

Check them out

Proudly supported by

Your logo could go here!

If you'd like to get your brand in front of attendees contact us.