This book lies at the interface of machine learning – a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail – and photonics – the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs).

Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible. In this book, the author pushes the performance of these systems significantly beyond what was achieved before. By interfacing a photonic reservoir computer with a high-speed electronic device (an FPGA), the author successfully interacts with the reservoir computer in real time, allowing him to considerably expand its capabilities and range of possible applications. Furthermore, the author draws on his expertise in machine learning and FPGA programming to make progress on a very different problem, namely the real-time image analysis of optical coherence tomography for atherosclerotic arteries.

本书位于机器学习（计算机科学的一个子领域，为具有挑战性的任务开发算法，如形状或图像识别，在传统算法失败的情况下）和光子学（光的物理科学，它是我们信息社会中使用的许多光通信技术的基础。它提供了一个关于储能计算和现场可编程门阵列（FPGA）的全面介绍。

最近，水库计算（一种基于人工神经网络的机器学习算法）的光子实现使光计算的突破成为可能。在这本书中，作者将这些系统的性能大大超出了以前的成就。通过将光子水库计算机与高速电子设备（FPGA）对接，作者成功地与水库计算机进行了实时互动，使他能够大大地扩展其能力和可能的应用范围。此外，作者利用他在机器学习和FPGA编程方面的专业知识，在一个非常不同的问题上取得了进展，即对动脉粥样硬化的光学相干断层扫描的实时图像分析。