In this paper, we present microring resonator (MRR) based polymorphic E-O circuits and architectures that can be employed for high-speed and energy-efficient non-binary reconfigurable computing. Our polymorphic E-O circuits can be dynamically programmed to implement different logic and arithmetic functions at different times. They can provide compactness and polymorphism to consequently improve operand handling, reduce idle time, and increase amortization of area and static power overheads. When combined with flexible photodetectors with the innate ability to accumulate a high number of optical pulses in situ, our circuits can support energy-efficient processing of data in non-binary formats such as stochastic/unary and high-dimensional reservoir formats. Furthermore, our polymorphic E-O circuits enable configurable E-O computing accelerator architectures for processing binarized and integer quantized convolutional neural networks (CNNs). We compare our designed polymorphic E-O circuits and architectures to several circuits and architectures from prior works in terms of area, latency, and energy consumption.

翻译：本文提出了一种基于微环谐振器（MRR）的多态电光（E-O）电路与架构，可用于实现高速、高能效的非二进制可重构计算。所提出的多态电光电路可通过动态编程，在不同时刻实现不同的逻辑与算术功能。这些电路具备紧凑性与多态性，从而优化操作数处理、减少空闲时间，并提高面积开销和静态功耗的分摊效率。结合具有原位累积大量光脉冲能力的柔性光电探测器，该电路可支持随机/一元以及高维储备池等非二进制格式数据的能效处理。此外，所述多态电光电路还可实现可配置的电光计算加速器架构，用于处理二值化和整数量化的卷积神经网络（CNN）。我们比较了所设计的多态电光电路与架构在面积、延迟和能耗方面与先前工作中的多种电路与架构的性能差异。