On September 28, 2017 at 10 am PT (1 pm ET), Dr. Vivienne Sze, Associate Professor in the Electrical Engineering and Computer Science Department at MIT (www.rle.mit.edu/eems), will present a free one-hour webinar, "Efficient Processing for Deep Learning: Challenges and Opportunities," organized by the Embedded Vision Alliance. Here's the description, from the event registration page:
Deep neural networks (DNNs) are proving very effective for a variety of challenging machine perception tasks. But these algorithms are very computationally demanding. To enable DNNs to be used in practical applications, it’s critical to find efficient ways to implement them. This webinar explores how DNNs are being mapped onto today’s processor architectures, and how both DNN algorithms and specialized processors are evolving to enable improved efficiency. Sze concludes with suggestions on how to evaluate competing processor solutions in order to address your particular application and design requirements. A question-and-answer session will follow the presentation.
UPDATE: Now that the live webinar is over, see here for the on-demand archive.