Abstract Microelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new device physics. Neural network models have come to dominate modern machine learning algorithms, and specialized electronic hardware has been developed to implement them more efficiently. A silicon photonic integration industry promises to bring manufacturing ecosystems normally reserved for microelectronics to photonics. Photonic devices have already found simple analog signal processing niches where electronics cannot provide sufficient bandwidth and reconfigurability. In order to solve more complex information processing problems, they will have to adopt a processing model that generalizes and scales. Neuromorphic photonics aims to map physical models of optoelectronic systems to abstract models of neural networks. It represents a new opportunity for machine information processing on sub-nanosecond timescales, with application to mathematical programming, intelligent radio frequency signal processing, and real-time control. The strategy of neuromorphic engineering is to externalize the risk of developing computational theory alongside hardware. The strategy of remaining compatible with silicon photonics externalizes the risk of platform development. In this perspective article, we provide a rationale for a neuromorphic photonics processor, envisioning its architecture and a compiler. We also discuss how it can be interfaced with a general purpose computer, i.e. a CPU, as a coprocessor to target specific applications. This paper is intended for a wide audience and provides a roadmap for expanding research in the direction of transforming neuromorphic photonics into a viable and useful candidate for accelerating neuromorphic computing.

中文翻译：

---

硅神经形态光子处理器入门：架构和编译器

摘要 微电子计算机在满足当今信息处理的所有需求方面遇到了挑战。满足这些需求将需要开发采用替代处理模型和新设备物理的非常规计算机。神经网络模型已经开始主导现代机器学习算法，并且已经开发出专门的电子硬件来更有效地实现它们。硅光子集成产业有望将通常为微电子保留的制造生态系统带入光子学。光子设备已经找到了简单的模拟信号处理利基，其中电子设备无法提供足够的带宽和可重构性。为了解决更复杂的信息处理问题，他们将不得不采用一种泛化和扩展的处理模型。神经形态光子学旨在将光电系统的物理模型映射到神经网络的抽象模型。它代表了亚纳秒时间尺度上机器信息处理的新机会，可应用于数学编程、智能射频信号处理和实时控制。神经形态工程的策略是将开发计算理论与硬件的风险外部化。保持与硅光子学兼容的策略将平台开发的风险外部化。在这篇透视文章中，我们提供了神经形态光子处理器的基本原理，设想了它的架构和编译器。我们还讨论了它如何与通用计算机接口，即 CPU，作为针对特定应用程序的协处理器。本文面向广大读者，并为在将神经形态光子学转变为加速神经形态计算的可行且有用的候选者的方向上扩展研究提供了路线图。