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nn-X: A Zynq-based low-power mobile coprocessor for accelerating deep neural networks

sleibso
Xilinx Employee
Xilinx Employee
0 0 44.1K

Deep neural networks that achieve state-of-the-art perception in both vision and auditory systems make sense of raw input data and parse them into symbols. A typical deep neural network consists of multiple convolution layers followed by a classification module. Deep neural network models are computationally very expensive, requiring up to billions of operations per second, usually requiring high-performance processors like server CPUs and GPUs to process large deep networks in real-time. Such processors consume a lot of power. A recent invited paper presented by a team from Purdue University at the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops held in Columbus, Ohio demonstrated a 240Gops mobile (low-power) processor for deep neural networks based on the Xilinx Zynq SoC. (“A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks.”)

 

The block diagram below shows the Zynq-based low-power mobile coprocessor for accelerating deep neural networks. It’s called “nn-X”for “neural network next.”

 

 

nnX Neural Network Coprocessor.png

 

nn-X: A Zynq-based low-power mobile coprocessor for accelerating deep neural networks

 

 

The coprocessor efficiently implements pipelined operators with large parallelism, delivering very high performance per unit power consumed. The nn-X coprocessor advances the state-of-the-art in multiple domains:

 

  • In data-streaming architectures
  • In efficient processing of deep neural networks
  • In providing high performance in real applications
  • In efficient use of system power

 

Work was done on a Xilinx ZC706 Eval Kit. For a full description of the design, please take a look at the paper referenced and linked above. For a quick look at what the coprocessor can do, here’s a face-recognition result on a couple of familiar faces taken from the paper:

 

 

Kirk and Spock.png

 

How about that? It even works on Vulcans.

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