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An ultra-low-voltage electronic implementation of inertial neuron model with nonmonotonous Liao's activation function

机译:具有非单调廖氏激活函数的惯性神经元模型的超低压电子实现

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The output of every neuron in neural network is specified by the employed activation function (AF) and therefore forms the heart of neural networks. As far as the design of artificial neural networks (ANNs) is concerned, hardware approach is preferred over software one because it promises the full utilization of the application potential of ANNs. Therefore, besides some arithmetic blocks, designing AF in hardware is the most important for designing ANN. While attempting to design the AF in hardware, the designs should be compatible with the modern Very Large Scale Integration (VLSI) design techniques. In this regard, the implemented designs should: only be in Metal Oxide Semiconductor (MOS) technology in order to be compatible with the digital designs, provide electronic tunability feature, and be able to operate at ultra-low voltage. Companding is one of the promising circuit design techniques for achieving these goals. In this paper, 0.5 V design of Liao's AF using sinh-domain technique is introduced. Furthermore, the function is tested by implementing inertial neuron model. The performance of the AF and inertial neuron model have been evaluated through simulation results, using the PSPICE software with the MOS transistor models provided by the 0.18-μm Taiwan Semiconductor Manufacturer Complementary Metal Oxide Semiconductor (TSM CMOS) process.
机译:神经网络中每个神经元的输出由所采用的激活函数(AF)指定,因此形成了神经网络的心脏。就人工神经网络(ANN)的设计而言,相比软件,首选硬件方法,因为它可以充分利用ANN的应用潜力。因此,除了一些算术块之外,在硬件中设计自动对焦对于设计人工神经网络来说也是最重要的。在尝试以硬件设计AF时,设计应与现代超大规模集成(VLSI)设计技术兼容。在这方面,已实现的设计应:仅采用金属氧化物半导体(MOS)技术,以便与数字设计兼容,提供电子可调性功能并能够在超低电压下运行。压扩是实现这些目标的有希望的电路设计技术之一。本文介绍了使用正弦域技术设计廖氏AF的0.5 V设计。此外,通过实现惯性神经元模型来测试该功能。 AF和惯性神经元模型的性能已通过仿真结果进行了评估,使用PSPICE软件以及0.18μm台湾半导体制造商互补金属氧化物半导体(TSM CMOS)工艺提供的MOS晶体管模型。

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