Low Power Neural Network Accelerators Based on Edge Deployment
Convolutional neural networks,as a deep learning model for processing network data,are widely used in in-dustries such as autonomous driving and aerospace.And with the growth of data volume,the structure of convolution-al network becomes more and more complex,for the convolutional network this kind of computation and resource-in-tensive network how to be deployed on the edge devices with low power consumption and few resources becomes a kind of difficulty.And FPGA can be used as an edge deployment device due to its high parallelism and low power consumption.On this basis,a gas pedal for LeNet-5 lightweight networks is proposed to maximize parallel computa-tion on FPGA using pipelined parallel acceleration and loop unfolding,and then use Vitis HLS to transform the high-level programming language into a hardware description language,and then use the Vitis IDE to write the software driver.Experimental results show that network inference on FPGA on ZYNQ reduces power consumption by a factor of 8 with similar detection rates,relative to network inference on CPU and GPU,which makes it an additional option for edge deployment of neural networks.
convolutional neural networkedge deploymentlow power consumptionFPGApipelineloop unrollingHLS
NETL
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