现场可编程门阵列
卷积神经网络
计算机科学
人工智能
人工神经网络
模式识别(心理学)
深度学习
领域(数学)
嵌入式系统
数学
纯数学
作者
Zhuwen Cai,Xin Zhang,Li Jun Jiang,Heng Zhang
标识
DOI:10.1109/icmtma54903.2022.00021
摘要
Convolutional neural networks (CNNs) have been widely used in the field of target recognition, and it has become a challenge to deploy convolutional neural networks with high accuracy and the ability to recognize multiple targets on a single chip. In order to save FPGA resources and improve target recognition accuracy with limited resources, this paper proposes an idea of constantly changing the weights (w) and biases (b) of the convolutional and fully connected layers in order to recognize different kinds of targets, and dynamically configuring w and b into the neural network to achieve the effect of improving target recognition accuracy[1]. The improved network is deployed on a Xilinx ZYNQ xc7z020 FPGA, which effectively improves the target recognition accuracy with fast recognition speed and low power consumption.
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