3-Layer BNN (Binary Neural Net) in Zynq UltraScale+ MPSoC boosts vision-guided robotic system performance by 11,320x!

2017年6月29日 | By News | Filed in: News.



Last year at Embedded World 2016, a vision-guided robot based on a Xilinx Zynq UltraScale+ ZU9 MPSoC incorporated into a ZCU102 eval kit autonomously played solitaire on an Android tablet in the Xilinx booth. (See “3D Delta Printer plays Robotic Solitaire on a Touchpad under control of a Xilinx Zynq UltraScale+ MPSoC.”) This year at Embedded World 2017, an upgraded and improved version of the robot again appeared in the Xilinx booth, still playing solitaire.


In the original implementation, an HD video camera monitored the Android tablet’s screen to image the solitaire playing cards. Acceleration hardware implemented in the Zynq MPSoC’s PL (programmable logic) performed real-time preprocessing of the HD video stream including Sobel edge detection. Software running on the Zynq MPSoC’s ARM Cortex-A53 APU (Application Processing Unit) recognized the playing cards from the processed video supplied by the Zynq MPSoC’s PL and planned the solitaire game moves for the robot. The Zynq MPSoC’s dual-core ARM Cortex-R5 RPU (Real-Time Processing Unit) operating in lockstep—useful for safety-critical applications such as robotic control—operated the robotic stylus positioner, fashioned from a 3D Delta printer. The other processing sections of the Zynq UltraScale+ ZU9 MPSoC were also gainfully employed in this demo.


This year a trained, 3-layer Convolutional BNN (Binary Neural Network) with 256 neurons/layer executed the playing-card recognition algorithm. The tangible results: improved accuracy and a performance boost of 11,320x! (Not to mention the offloading of the recognition task from the Zynq MPSoC’s APU.)


Here’s a new, 2-minute video explaining the new autonomous solitaire-playing demo system:





Note: For more information about BNNs and programmable logic, see:







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June 29, 2017 at 01:32AM


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