Photonic computing is a compelling avenue for performing highly efficient matrix multiplication, a crucial operation in Deep Neural Networks (DNNs). While this method has shown great success in DNN inference, meeting the high precision demands of DNN training proves challenging due to the precision limitations imposed by costly data converters and the analog noise inherent in photonic hardware. This paper proposes Mirage, a photonic DNN training accelerator that overcomes the precision challenges in photonic hardware using the Residue Number System (RNS). RNS is a numeral system based on modular arithmetic$\unicode{x2014}$allowing us to perform high-precision operations via multiple low-precision modular operations. In this work, we present a novel micro-architecture and dataflow for an RNS-based photonic tensor core performing modular arithmetic in the analog domain. By combining RNS and photonics, Mirage provides high energy efficiency without compromising precision and can successfully train state-of-the-art DNNs achieving accuracy comparable to FP32 training. Our study shows that on average across several DNNs when compared to systolic arrays, Mirage achieves more than $23.8\times$ faster training and $32.1\times$ lower EDP in an iso-energy scenario and consumes $42.8\times$ lower power with comparable or better EDP in an iso-area scenario.

翻译：光子计算是实现深度神经网络（DNNs）中关键操作——矩阵乘法的高效途径。尽管该方法在 DNN 推理中表现卓越，但由于昂贵的数据转换器带来的精度限制以及光子硬件固有的模拟噪声，满足 DNN 训练所需的高精度要求颇具挑战。本文提出 Mirage，一种基于残数系统（RNS）的光子 DNN 训练加速器，该加速器通过 RNS 克服了光子硬件中的精度难题。RNS 是一种基于模运算的数值系统，允许我们通过多个低精度模运算来实现高精度操作。在本工作中，我们提出了一种新颖的微架构和数据流，用于基于 RNS 的光子张量核，该核在模拟域中执行模运算。通过结合 RNS 与光子学，Mirage 在不牺牲精度的前提下提供了高能效，能够成功训练最先进的 DNNs，并达到与 FP32 训练相当的准确率。我们的研究表明，与脉动阵列相比，在多个 DNNs 上的平均表现中，Mirage 在等能量场景下实现了超过 23.8× 的更快训练速度和 32.1× 更低的 EDP（能量延迟积），在等面积场景下则实现了 42.8× 的更低功耗，同时 EDP 相当或更优。