April 18–19, 2018 Boston Convention & Exhibition Center Boston, MA

ESC Boston 2018 Schedule Viewer

Use the scheduling tool below to browse all the available sessions, speakers and topics at this year's event. Find the content and sessions to fit all of your educational needs and ensure you get the most out of your time at the show.

Accelerating Neural Networks for Autonomous Driving Systems via FPGAs

Speakers:

Glenn Steiner (Senior Manager, Xilinx)

Michaela Blott (Principal Engineer, Xilinx)

Andreas Schuler (Director, Missing Link Electronics)

Date: Wednesday, April 18

Time: 8:00am - 8:45am

Pass type: Conference (Paid) - Get your pass now!

Free Content & Activities: N/A

Conference Track: Advanced Technologies, Embedded Software Design & Verification

Vault Recording: TBD

Audience Level: N/A

For many applications, researchers and now developers are moving to Convolutional Neural Networks (CNN) as the best approach for object detection and identification. Traditionally, CNNs have been implemented in floating point on general-purpose computers. Unfortunately, this does not scale well to embedded processors, which are typically cost and power constrained. The latest implementations of neural networks have moved to integer operations ranging from 32 bits down to 8 bits. However, to accommodate the massive number of operations required for vision recognition at real-time frame rates GPUs, ASICS or FPGAs have been required. FPGAs have the unique ability to be configured for the integer precision required for any given application enabling less table memory, increased performance, and lower power consumption compared to traditional integer implementations on competitive devices. In this session we will examine implementations of a Binary Neural Network (BNN) on an FPGA demonstrating four orders of magnitude greater performance than a software implementation on an embedded processor. And, we will provide a detailed example of a traffic sign recognition system including real-time camera input, sign identification, and recognition.

Takeaway

Convolutional Neural Networks (CNN) are becoming the go-to solution for object detection and identification. In this session we will examine implementation of a Binary Neural Network (BNN) for real-time object recognition on an FPGA demonstrating four orders of magnitude greater performance than a software implementation on an embedded processor.