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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 (44,151 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: