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Vinod Kathail, Distinguished Engineer and leader of the Embedded Vision team at Xilinx, presents the "Caffe to Zynq: State-of-the-Art Machine Learning Inference Performance in Less Than 5 Watts" tutorial at the May 2017 Embedded Vision Summit.

Machine learning research is advancing daily with new network architectures, making it difficult to choose the best CNN algorithm for a particular application. With this rapid rate of change in algorithms, embedded system developers who require high performance and low power consumption are increasingly considering Zync SoCs. Zynq SoCs are ideal for efficient CNN implementation as they allow creation of custom network circuitry in hardware, tuned exactly to the needs of the algorithm. The result is state-of-the-art performance-per-watt that outstrips CPU- and GPU-based embedded systems. In this talk, Kathail presents a method for easily migrating a CNN running in Caffe to an efficient Zynq-based embedded vision system utilizing Xilinx’s new reVISION software stack.

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