This work presents ROSA, a microring-based optical neural network architecture that improves robustness and energy efficiency using an optical shift-and-add (OSA) module and a layer-wise hybrid mapping strategy. It introduces a noise-aware voltage-to-weight model considering DAC and thermal variations, and a workload-aware framework to co-optimize MRR array size and layer-wise dataflow. Optimized arrays reduce the aggregated relative energy-delay product (EDP) by 64% and 26% compared with DEAP-CNNs and a general compact array, respectively. OSA further contributes 29% EDP reduction. The proposed hybrid mapping strategy improves CIFAR-10 accuracy by 8.3% over weight-stationary mapping while achieving an average 54.7% lower EDP than DEAP-CNNs.

翻译：本文提出ROSA，一种基于微环的光学神经网络架构，通过光学移位相加模块和逐层混合映射策略提升鲁棒性与能效。该架构引入考虑数模转换器与热变异的噪声感知电压-权重模型，以及面向工作负载的协同优化框架，用于联合优化微环谐振器阵列规模与逐层数据流。优化后的阵列相比DEAP-CNNs与常规紧凑阵列，聚合相对能量延迟积分别降低64%和26%；光学移位相加模块进一步贡献29%的EDP降幅。所提出的混合映射策略在权重固定映射基础上将CIFAR-10精度提升8.3%，同时实现较DEAP-CNNs平均54.7%的更优EDP。