Memcomputing加速优化
Memcomputing for Accelerated Optimization
摘要：
在这项工作中，我们介绍了一个全新的电路架构的概念，基于新颖的，物理启发的计算范式：Memcomputing。特别是，我们专注于数字记忆计算机（DMMs），可以设计利用非线性动态系统的属性，最终描述的电子电路。这些系统的工作原理依赖于电路的电流和电压的自组织能力，以满足数学关系。特别是对于这项工作，我们讨论自组织门，即自组织代数门（SOAGs），旨在解决线性不等式，因此用于解决线性规划（ILP）格式的优化问题。与传统的I/O门不同，SOAG是终端不可知的，这意味着每个终端处理输入和输出信号的叠加。当适当地组装来表示给定的ILP问题时，相应的自组织电路收敛到表达手头问题的解决方案的平衡点。由于DMM的组件是非量子的，因此描述它的常微分方程可以在我们的现代计算机上以软件形式有效地模拟，也可以通过现成的技术内置在硬件中。作为一个例子，我们展示了这种新的方法实现为软件即服务（MemCPU XPC），以解决ILP问题的性能。与当今使用世界知名的商业求解器找到的最佳解决方案相比，MemCPU XPC将解决方案的时间从23小时缩短到不到2分钟。
英文摘要：
In this work, we introduce the concept of an entirely new circuit architecture based on the novel, physics-inspired computing paradigm: Memcomputing. In particular, we focus on digital memcomputing machines (DMMs) that can be designed leveraging properties of non-linear dynamical systems; ultimate descriptors of electronic circuits. The working principle of these systems relies on the ability of currents and voltages of the circuit to self-organize in order to satisfy mathematical relations. In particular for this work, we discuss self-organizing gates, namely Self-Organizing Algebraic Gates (SOAGs), aimed to solve linear inequalities and therefore used to solve optimization problems in Integer Linear Programming (ILP) format. Unlike conventional IØgates, SOAGs are terminal-agnostic, meaning each terminal handles a superposition of input and output signals. When appropriately assembled to represent a given ILP problem, the corresponding self-organizing circuit converges to the equilibria that express the solutions to the problem at hand. Because DMM's components are non-quantum, the ordinary differential equations describing it can be efficiently simulated on our modern computers in software, as well as be built in hardware with off-of-the-shelf technology. As an example, we show the performance of this novel approach implemented as Software as a Service (MemCPU XPC) to address an ILP problem. Compared to today's best solution found using a world renowned commercial solver, MemCPU XPC brings the time to solution down from 23 hours to less than 2 minutes.
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。
