Abstract: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.
| Subjects: | Hardware Architecture (cs.AR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.00032 [cs.AR] |
| (or arXiv:2605.00032v1 [cs.AR] for this version) | |
| https://doi.org/10.48550/arXiv.2605.00032 arXiv-issued DOI via DataCite (pending registration) |
From: Huifan Zhang [view email]
[v1]
Fri, 24 Apr 2026 04:45:04 UTC (774 KB)
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