记忆计算机的研究与探讨
A Survey and Discussion of Memcomputing Machines
摘要：
本文对记忆计算的最新研究成果进行了回顾和讨论。特别地，讨论了$\textit{universal memcomputing machine}$（UMM）和$\textit{digital memcomputing machine}$（DMM）。我们回顾memcomputing概念的动力系统框架和评估算法提供的计算$NP$问题的UMM和DMM范式。我们认为，UMM是一个物理上难以置信的机器，和DMM模型，所描述的数值模拟，是没有更强大的图灵完备计算。我们声称，$P$与$NP$的决议的证据，因此是不确定的，并得出结论，memcomputing机器范式构成了一个能源效率，特殊用途的一类模型的动力系统计算。
英文摘要：
This paper serves as a review and discussion of the recent works on memcomputing. In particular, the $\textit{universal memcomputing machine}$ (UMM) and the $\textit{digital memcomputing machine}$ (DMM) are discussed. We review the memcomputing concept in the dynamical systems framework and assess the algorithms offered for computing $NP$ problems in the UMM and DMM paradigms. We argue that the UMM is a physically implausible machine, and that the DMM model, as described by numerical simulations, is no more powerful than Turing-complete computation. We claim that the evidence for the resolution of $P$ vs. $NP$ is therefore inconclusive, and conclude that the memcomputing machine paradigm constitutes an energy efficient, special-purpose class of models of dynamical systems computation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
