Over the past decade alternative technologies have gained momentum as conventional digital electronics continue to approach their limitations, due to the end of Moore's Law and Dennard Scaling. At the same time, we are facing new application challenges such as those due to the enormous increase in data. The attention, has therefore, shifted from homogeneous computing to specialized heterogeneous solutions. As an example, brain-inspired computing has re-emerged as a viable solution for many applications. Such new processors, however, have widened the abstraction gamut from device level to applications. Therefore, efficient abstractions that can provide vertical design-flow tools for such technologies became critical. Photonics in general, and neuromorphic photonics in particular, are among the promising alternatives to electronics. While the arsenal of device level toolbox for photonics, and high-level neural network platforms are rapidly expanding, there has not been much work to bridge this gap. Here, we present a design methodology to mitigate this problem by extending high-level hardware-agnostic neural network design tools with functional and performance models of photonic components. In this paper we detail this tool and methodology by using design examples and associated results. We show that adopting this approach enables designers to efficiently navigate the design space and devise hardware-aware systems with alternative technologies.

中文翻译：

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后摩尔定律加速器的设计方法：光子神经形态处理器的案例

在过去十年中，由于摩尔定律和登纳德标度的终结，传统数字电子产品不断接近其局限性，替代技术获得了动力。与此同时，我们正面临着新的应用挑战，例如由于数据的巨大增长而带来的挑战。因此，注意力已经从同构计算转移到专门的异构解决方案。例如，受脑启发的计算已重新成为许多应用程序的可行解决方案。然而，此类新处理器已将抽象范围从设备级扩展到应用程序。因此，可以为此类技术提供垂直设计流程工具的有效抽象变得至关重要。一般而言，光子学，特别是神经形态光子学，是电子产品的有前途的替代品之一。虽然用于光子学的设备级工具箱和高级神经网络平台正在迅速扩大，但并没有太多工作来弥合这一差距。在这里，我们提出了一种设计方法，通过扩展具有光子组件功能和性能模型的高级硬件不可知神经网络设计工具来缓解这个问题。在本文中，我们通过使用设计示例和相关结果详细介绍了此工具和方法。我们表明，采用这种方法使设计人员能够有效地导航设计空间并设计具有替代技术的硬件感知系统。我们提出了一种设计方法，通过扩展具有光子组件功能和性能模型的高级硬件不可知神经网络设计工具来缓解这个问题。在本文中，我们通过使用设计示例和相关结果详细介绍了此工具和方法。我们表明，采用这种方法使设计人员能够有效地导航设计空间并设计具有替代技术的硬件感知系统。我们提出了一种设计方法，通过扩展具有光子组件功能和性能模型的高级硬件不可知神经网络设计工具来缓解这个问题。在本文中，我们通过使用设计示例和相关结果详细介绍了此工具和方法。我们表明，采用这种方法使设计人员能够有效地导航设计空间并设计具有替代技术的硬件感知系统。