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Xilinx FPGA Enables Neural Networks

Posted by Danielle Leger

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May 15, 2017 8:06:00 AM

FPGA are being used as the hardware required for training deep neural networks (DNN) and other machine learning algorithms. A neural network is a computer system modeled on the human brain and nervous system. 



One of the developing areas for FPGA is in the use of binarized neural networks (BNN), a method that has implications on the low-precision side for both hardware and software. Binarized neural networks are gaining interest due to their significantly lower computational and memory cost and are well suited to reconfigurable logic devices, such as FPGA.

Xilinx's research results are based on the Xilinx ADM-PCIE-8K5 and show very high image classification rates and very high power efficiency which can be achieved by mapping BNNs to FPGAs.

The ADM-PCIE-7V3 is a high performance reconfigurable Half-Length, low profile x8 PCIe form factor board based on the Xilinx Virtex-7 range of Platform FPGAs. It features high speed I/O, SATA connections, voltage/temperature/current control and monitoring, passive air-cooled heat sink and other highlights. 


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Xilinx researchers developed a framework for efficient, scalable BNN inference on an FPGA, called FINN.

FINN utilizes a set of optimizations that enable efficient mapping of BNNs to hardware and implemented fully connected, non-padded convolutional and pooling layers.

FINN-generated accelerators are ideal for supporting real-time embedded applications, such as augmented reality, autonomous driving, and robotics.



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Topics: FPGA

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