论文标题
光子储层计算机中高性能模拟读数层的在线培训
Online training for high-performance analogue readout layers in photonic reservoir computers
论文作者
论文摘要
介绍。储层计算是一种用于处理时间相关信号的生物启发的计算范式。其硬件实现的性能与一系列基准任务上的最新数字算法相媲美。这些实现的主要瓶颈是基于慢速离线后处理的读数层。很少有人提出模拟解决方案，但由于设置的复杂性增加，所有这些解决方案都遭受了通知能力下降的损失。方法。在这里，我们建议使用在线培训解决这些问题。我们使用具有模拟读数层的实验可行储层计算机的数值模拟来研究此方法的适用性。我们还考虑了一个非线性输出层，使用传统方法很难训练。结果。我们以数字显示在线学习可以规避模拟层的附加复杂性，并获得与数字层相同的性能水平。结论。这项工作为通过在线培训输出层的在线培训提供了高性能的全障碍储层计算机的方式铺平了道路。
Introduction. Reservoir Computing is a bio-inspired computing paradigm for processing time-dependent signals. The performance of its hardware implementation is comparable to state-of-the-art digital algorithms on a series of benchmark tasks. The major bottleneck of these implementation is the readout layer, based on slow offline post-processing. Few analogue solutions have been proposed, but all suffered from notice able decrease in performance due to added complexity of the setup. Methods. Here we propose the use of online training to solve these issues. We study the applicability of this method using numerical simulations of an experimentally feasible reservoir computer with an analogue readout layer. We also consider a nonlinear output layer, which would be very difficult to train with traditional methods. Results. We show numerically that online learning allows to circumvent the added complexity of the analogue layer and obtain the same level of performance as with a digital layer. Conclusion. This work paves the way to high-performance fully-analogue reservoir computers through the use of online training of the output layers.
