Liu Yixuan, Hu Qiao, Wu Qiqiao, Liu Xuanzhi, Zhao Yulin, Zhang Donglin, Han Zhongze, Cheng Jinhui, Ding Qingting, Han Yongkang, Peng Bo, Jiang Haijun, Xue Xiaoyong, Lv Hangbing, Yang Jianguo
Zhejiang Lab, Hangzhou 311121, China.
School of Microelectronics, Fudan University, Shanghai 200433, China.
Micromachines (Basel). 2022 Jun 10;13(6):924. doi: 10.3390/mi13060924.
Probabilistic computing is an emerging computational paradigm that uses probabilistic circuits to efficiently solve optimization problems such as invertible logic, where traditional digital computations are difficult to solve. This paper proposes a true random number generator (TRNG) based on resistive random-access memory (RRAM), which is combined with an activation function implemented by a piecewise linear function to form a standard p-bit cell, one of the most important parts of a p-circuit. A p-bit multiplexing strategy is also applied to reduce the number of p-bits and improve resource utilization. To verify the superiority of the proposed probabilistic circuit, we implement the invertible p-circuit on a field-programmable gate array (FPGA), including AND gates, full adders, multi-bit adders, and multipliers. The results of the FPGA implementation show that our approach can significantly save the consumption of hardware resources.
概率计算是一种新兴的计算范式，它使用概率电路来有效解决诸如可逆逻辑等传统数字计算难以解决的优化问题。本文提出了一种基于电阻式随机存取存储器（RRAM）的真随机数发生器（TRNG），该发生器与由分段线性函数实现的激活函数相结合，形成一个标准的p位单元，这是p电路最重要的部分之一。还应用了一种p位复用策略来减少p位的数量并提高资源利用率。为了验证所提出的概率电路的优越性，我们在现场可编程门阵列（FPGA）上实现了可逆p电路，包括与门、全加器、多位加法器和乘法器。FPGA实现的结果表明，我们的方法可以显著节省硬件资源的消耗。
