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How can you classify >1800 images/sec @ < 50W? Xilinx Kintex UltraScale FPGA + xDNN Library + AlexNet + Caffe

by Xilinx Employee ‎11-22-2016 02:12 PM - edited ‎11-22-2016 02:19 PM (43,640 Views)

 

Want to see how fast machine inference can go and how efficient it can be? The video below shows you how fast the AlexNet image-classification algorithm runs (better than 1800 image classifications/sec)—and how efficiently it runs (<50W)—using an INT8 (8-bit integer) implementation. The demo on the video shows AlexNet running in an open-source Caffe deep-learning framework, implemented with the xDNN deep neural network library running on a Xilinx UltraScale FPGA in the Xilinx Kintex UltraScale FPGA Acceleration Development Kit.

 

All of the above components are part of the newly announced Xilinx Reconfigurable Acceleration Stack.

 

Note: If you implemented this classification application using INT16 instead, you’d get about half the performance, as mentioned in the video and discussed in detail in the previous Xcell Daily blog post, “Counter-Intuitive: Fixed-Point Deep-Learning Inference Delivers 2x to 6x Better CNN Performance with Great Accuracy.”

 

Here’s the video showing FPGA-based image classification in action:

 

 

 

 

 

 

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About the Author
  • Be sure to join the Xilinx LinkedIn group to get an update for every new Xcell Daily post! ******************** Steve Leibson is the Director of Strategic Marketing and Business Planning at Xilinx. He started as a system design engineer at HP in the early days of desktop computing, then switched to EDA at Cadnetix, and subsequently became a technical editor for EDN Magazine. He's served as Editor in Chief of EDN Magazine, Embedded Developers Journal, and Microprocessor Report. He has extensive experience in computing, microprocessors, microcontrollers, embedded systems design, design IP, EDA, and programmable logic.