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Gesture Recognition Using Neural Networks Based on HW/SW Cosimulation Platform

机译:基于硬件/软件协同仿真平台的神经网络手势识别

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Hardware/software (HW/SW) cosimulation integrates software simulation and hardware simulation simultaneously. Usually, HW/SW co-simulation platform is used to ease debugging and verification for very large-scale integration (VLSI) design. To accelerate the computation of the gesture recognition technique, an HW/SW implementation using field programmable gate array (FPGA) technology is presented in this paper. The major contributions of this work are: (1) a novel design of memory controller in the Verilog Hardware Description Language (Verilog HDL) to reduce memory consumption and load on the processor. (2) The testing part of the neural network algorithm is being hardwired to improve the speed and performance. The American Sign Language gesture recognition is chosen to verify the performance of the approach. Several experiments were carried out on four databases of the gestures (alphabet signs A to Z). (3) The major benefit of this design is that it takes only few milliseconds to recognize the hand gesture which makes it computationally more efficient.
机译:硬件/软件(HW / SW)协同仿真同时集成了软件仿真和硬件仿真。通常,硬件/软件协同仿真平台用于简化超大规模集成(VLSI)设计的调试和验证。为了加速手势识别技术的计算,本文提出了使用现场可编程门阵列(FPGA)技术的硬件/软件实现。这项工作的主要贡献是:(1)采用Verilog硬件描述语言(Verilog HDL)的存储器控​​制器的新颖设计,以减少存储器消耗和处理器上的负载。 (2)对神经网络算法的测试部分进行了硬连线,以提高速度和性能。选择美国手语手势识别以验证该方法的性能。在四个手势数据库(字母符号A到Z)上进行了几次实验。 (3)此设计的主要好处是识别手势仅需几毫秒,从而使计算效率更高。

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