论文标题
混乱逻辑门的灵敏度
Sensitivity of a Chaotic Logic Gate
论文作者
论文摘要
混乱的逻辑门或“混沌”是设计通用计算机的有前途的混合信号方法。但是，混沌系统对小扰动呈指数敏感，噪声的影响会导致混乱的计算机失败。在这里，我们检查了模拟的陈词滥调对噪声和其他参数变化（例如电源电压差异）的敏感性。我们发现，参数空间中的区域与混沌动力学相对应与计算中最大误差区域相吻合。此外，此误差在混乱地图的4-10次迭代中呈指数增长。因此，我们讨论了混乱计算的基本局限性，并提出了潜在的改进。我们的Python仿真方法是开源的，可在https://github.com/noeloikeau/chaogate上找到。
Chaotic logic gates or `chaogates' are a promising mixed-signal approach to designing universal computers. However, chaotic systems are exponentially sensitive to small perturbations, and the effects of noise can cause chaotic computers to fail. Here, we examine the sensitivity of a simulated chaogate to noise and other parameter variations (such as differences in supply voltage). We find that the regions in parameter space corresponding to chaotic dynamics coincide with the regions of maximum error in the computation. Further, this error grows exponentially within 4-10 iterations of the chaotic map. As such, we discuss the fundamental limitations of chaotic computing, and suggest potential improvements. Our Python simulation methods are open-source and available at https://github.com/Noeloikeau/chaogate.
