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Mountain View, CA
93 West El Camino Real, Mountain View, California 94040
24Jul
Squeezing down the computing requirements of deep neural networks
6:00 PM - 11:59 PM 673 South Milpitas Blvd
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Date: July 24, 2019 to July 24, 2019
Where: 673 South Milpitas Blvd, Milpitas, California, United States
Phone: N/A
Event Type: Business
Ticket Price: N/A
Monday, July 24, 2019, 6:00 PM - 8:00 PM PDT  The event is FREE. Food & drinks will be provided. This event is organized by: IEEE Computer Society (CS) of Silicon Valley  Co-sponsors:  Computational Intelligence Society (CIS)  Solid State Circuit Society (SSCS)   ValleyML.ai  IEEE Circuit & Systems Society (CAS)  Association of Computer Machinery (ACM) Speaker:  Forrest Iandola, CEO and co-Founder of DeepScale   Location: 673 South Milpitas Blvd. Milpitas, CA 95035, USA   PROGRAM: 6:00 - 6:30 PM Networking & Refreshments 6:30 - 7:30 PM Talk 7:30 - 8:00 PM Q&A/Adjourn Abstract: Deep Neural Networks (DNNs) have enabled breakthrough levels of accuracy on a variety of tasks in vision, audio, and text. However, DNNs can be quite computationally-intensive, and highly-accurate DNNs often require a full-sized GPU server for real-time inference. To squeeze DNNs into smaller computing footprints, there are a number of techniques, including better DNN design, DNN quantization, better implementations of DNNs, and better utilization of specialized computing hardware. This talk touches on all these techniques, with particular focus on better DNN design for computer vision. Recently, Neural Architecture Search (NAS) technologies have begun to make significant progress in automating the process of designing "squeezed" DNNs, and we cover some of the latest work on NAS in this talk. Bio: Forrest Iandola  Forrest Iandola completed a PhD in Electrical Engineering and Computer Science at UC Berkeley, where his research focused on improving the efficiency of deep neural networks (DNNs). His best-known published research ranges from scaling DNN training to hundreds of GPUs (FireCaffe), to squeezing DNNs onto small edge-devices (SqueezeNet and SqueezeDet). His advances in scalable training and efficient inference of DNNs led to the founding of DeepScale, where he has been CEO since 2015. DeepScale builds energy-efficient vision and perception systems for automated vehicles. Open
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