Computing hyperbolic tangent and sigmoid functions using stochastic logic
Computing hyperbolic tangent and sigmoid functions using stochastic logic
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使用随机逻辑计算双曲正切和 S 型函数
DOI：
10.1109/acssc.2016.7869645
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
K. Parhi
中科院分区：
文献类型：
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作者：
Yin Liu;K. Parhi
本文讨论了用随机逻辑实现正切双曲函数和s型函数。随机计算需要简单的逻辑门，并且具有固有的容错性。因此，这些结构非常适合纳米级CMOS技术。切线双曲函数和s型函数在神经网络等机器学习系统中有着广泛的应用。本文有两个主要贡献。首先，提出了实现单极随机逻辑中正切双曲函数和s型函数的两种方法。第一种方法是基于JK触发器。在第二种方法中，提出的设计基于一般单极划分。其次，我们给出了计算双极随机逻辑中正切双曲函数和s型函数的两种方法。第一种方法涉及从双极格式到单极格式的格式转换。第二种方法使用一般双极随机分压器。给出了所提设计的仿真和综合结果。
This paper addresses implementations of tangent hyperbolic and sigmoid functions using stochastic logic. Stochastic computing requires simple logic gates and is inherently fault-tolerant. Thus, these structures are well suited for nanoscale CMOS technologies. Tangent hyperbolic and sigmoid functions are widely used in machine learning systems such as neural networks. This paper makes two major contributions. First, two approaches are proposed to implementing tangent hyperbolic and sigmoid functions in unipolar stochastic logic. The first approach is based on a JK flip-flop. In the second approach, the proposed designs are based on a general unipolar division. Second, we present two approaches to computing tangent hyperbolic and sigmoid functions in bipolar stochastic logic. The first approach involves format conversion from bipolar format to unipolar format. The second approach uses a general bipolar stochastic divider. Simulation and synthesis results are presented for proposed designs.