# ROSA: Robust and Energy-Efficient Microring-Based Optical Neural Networks via Optical Shift-and-Add and Layer-Wise Hybrid Mapping

arXiv: https://arxiv.org/abs/2605.00032 · HTML: https://mlsanity.com/paper/2605.00032/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [Neuromorphic Silicon Photonic Networks](https://mlsanity.com/paper/1611.02272/) (2016)
- [CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Network Accelerator](https://mlsanity.com/paper/2102.06960/) (2021)
- [Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)](https://mlsanity.com/paper/1907.01525/) (2019)
- [Lightening-Transformer: A Dynamically-operated Optically-interconnected Photonic Transformer Accelerator](https://mlsanity.com/paper/2305.19533/) (2023)
- [CiMLoop: A Flexible, Accurate, and Fast Compute-In-Memory Modeling Tool](https://mlsanity.com/paper/2405.07259/) (2024)
- [O-HAS: Optical Hardware Accelerator Search for Boosting Both Acceleration Performance and Development Speed](https://mlsanity.com/paper/2108.07538/) (2021)
- [Architecture-Level Modeling of Photonic Deep Neural Network Accelerators](https://mlsanity.com/paper/2405.07266/) (2024)
- [Modeling Analog-Digital-Converter Energy and Area for Compute-In-Memory Accelerator Design](https://mlsanity.com/paper/2404.06553/) (2024)
- [Toward Lifelong-Sustainable Electronic-Photonic AI Systems via Extreme Efficiency, Reconfigurability, and Robustness](https://mlsanity.com/paper/2509.07396/) (2025)

## Abstract (arXiv)

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.