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DeePhi launches vision-processing dev boards based on a Zynq SoC and Zynq UltraScale+ MPSoC, companion Neural Network (NN) dev kit

Xilinx Employee
Xilinx Employee
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Yesterday, DeePhi Tech announced several new deep-learning products at an event held in Beijing. All of the products are based on DeePhi’s hardware/software co-design technologies for neural network (NN) and AI development and use deep compression and Xilinx All Programmable technology as a foundation. Central to all of these products is DeePhi’s Deep Neural Network Development Kit (DNNDK), an integrated framework that permits NN development using popular tools and libraries such as Caffe, TensorFlow, and MXNet to develop and compile code for DeePhi’s DPUs (Deep Learning Processor Units). DeePhi has developed two FPGA-based DPUs: the Aristotle Architecture for convolutional neural networks (CNNs) and the Descartes Architecture for Recurrent Neural Networks (RNNs).

 

 

DeePhi DNNDK Design Flow.jpg 

 

DeePhi’s DNNDK Design Flow

 

 

 

DeePhi Aristotle Architecture.jpg 

 

DeePhi’s Aristotle Architecture

 

 

 

DeePhi Descartes Architecture.jpg 

 

DeePhi’s Descartes Architecture

 

 

 

DeePhi’s approach to NN development using Xilinx All Programmable technology uniquely targets the company’s carefully optimized, hand-coded DPUs instantiated in programmable logic. In the new book “FPGA Frontiers” published the Next Platform Press, DeePhi’s co-founder and CEO Song Yao describes using his company’s DPUs: “The algorithm designer doesn’t need to know anything about the underlying hardware. This generates instruction instead of RTL code, which leads to compilation in 60 seconds.” The benefits are rapid development and the ability to concentrate on NN code development rather than the mechanics of FPGA compilation, synthesis, and placement and routing.

 

Part of yesterday’s announcement included two PCIe boards oriented towards vision processing that implement DeePhi’s Aristotle Architecture DPU. One board, based on the Xilinx Zynq Z-7020 SoC, handles real-time CNN-based video analysis including facial detection for more than 30 faces simultaneously for 1080p, 18fps video using only 2 to 4 watts. The second board, based on a Xilinx Zynq UltraScale+ ZU9 MPSoC, supports simultaneous, real-time video analysis for 16 channels of 1080p, 18fps video and draws only 30 to 60 watts.

 

DeePhi Zynq SoC PCIe card.jpg 

 

DeePhi PCIe NN board based on a Xilinx Zynq Z-7020 SoC

 

 

 

DeePhi PCIe NN card based on Zynq UltraScale Plus MPSoC .jpg 

 

DeePhi PCIe NN board based on a Xilinx Zynq UltraScale+ ZU9 MPSoC

 

 

 

For more information about these products, please contact DeePhi Tech directly.