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Efficient Hardware Implementation of the Hyperbolic Tangent Sigmoid Function

机译:高效的硬件实现双曲线切线型函数

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Efficient implementation of the activation function is important in the hardware design of artificial neural networks. Sigmoid, and hyperbolic tangent sigmoid functions are the most widely used activation functions for this purpose. In this paper, we present a simple and efficient architecture for digital hardware implementation of the hyperbolic tangent sigmoid function. The proposed method employs a piecewise linear approximation as a foundation, and further improves the results using a lookup table. Our design proves to be more efficient considering area X delay as a performance metric when compared to similar proposals. VLSI implementation of the proposed design using a 0.18 (mu)m CMOS process is also presented, which shows a 35percent improvement over similar recently published architectures.
机译:在人工神经网络的硬件设计中有效地实现激活功能。 SIGMOID和双曲线切线函数是此目的最广泛使用的激活功能。在本文中,我们为双曲线切线凝固函数的数字硬件实现提供了一种简单而有效的架构。该方法采用分段线性近似作为基础,并进一步使用查找表来改进结果。我们的设计证明,与类似提案相比,考虑到X延迟作为绩效指标,将其设计更有效。还介绍了使用0.18(MU)M CMOS工艺的提出设计的VLSI实现,其显示出在相似最近发表的架构上的35%的改进。

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