Session ID: HKG18-405 Session Name: HKG18-405 - Accelerating Neural Networks for Vision Systems via FPGAs Speaker: Glenn Steiner Track: IoT, Embedded

Session Summary

The performance and accuracy of Convolutional Neural Networks for visual recognition has reached the point where researchers generally consider them to be more accurate than traditional algorithmic approaches. In this session we will examine implementation of a Binary Neural Network (BNN) on an FPGA with embedded processing system demonstrating four orders of magnitude greater performance than a software implementation on an embedded processor. We will start with the basic concepts of Convolutional Neural Networks. Next, we will examine why FPGAs with embedded processors provide the necessary flexibility to accommodate network precision as well as varying number of neurons and layers. Finally, we will demonstrate a BNN running on a 96 board doing real-time traffic recognition at over 8,000 images per second. —————————————————

Resources

Event Page: http://connect.linaro.org/resource/hkg18/hgk18-405/ Presentation: http://connect.linaro.org.s3.amazonaws.com/hkg18/presentations/hgk18-405.pdf Video: http://connect.linaro.org.s3.amazonaws.com/hkg18/videos/hgk18-405.mp4 —————————————————

Event Details

hkg18 19-23 March 2018 Regal Airport Hotel Hong Kong


Keyword: IoT, Embedded ‘http://www.linaro.org’ ‘http://connect.linaro.org’ ————————————————— Follow us on Social Media https://www.facebook.com/LinaroOrg https://www.youtube.com/user/linaroorg?sub_confirmation=1 https://www.linkedin.com/company/1026961