Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.12934 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908911555051520 |
|---|---|
| author | Park, Hyoseok Park, Yeonsang |
| author_facet | Park, Hyoseok Park, Yeonsang |
| contents | The rapid growth of large-scale AI models has intensified energy consumption and data-movement challenges in modern datacenters. Photonic accelerators offer a promising path by executing the linear matrix multiplications of transformer inference at high throughput and low energy. However, the softmax attention layer, which requires element-wise exponentiation followed by normalization, still relies on electronic post-processing, creating an electro-optic conversion bottleneck that negates much of the potential photonic advantage. We present a cascaded micro-ring resonator (MRR) architecture that synthesizes the per-channel exponential function required by softmax, e^{x_n - max(x)}, over a finite interval with tunable worst-case relative error. A control signal detunes each ring via an electro-optic mechanism; a weak probe at fixed frequency experiences Lorentzian transmission, and cascading N identical stages yields a multiplicative transfer function whose logarithm is approximately linear. We derive mapping rules, depth-scaling estimates, and a minimax fitting formulation, and validate the framework with three-dimensional FDTD simulations of X-cut thin-film lithium niobate (TFLN) add-drop micro-ring resonators. Direct multi-ring FDTD validation extends to a five-ring cascade and confirms agreement with theory primarily over the upper operating range; deeper cascades and higher quality factors are assessed analytically. The cascade implements the per-channel exponential block, the key missing nonlinearity for photonic softmax. We further present a WDM-parallel chip architecture with closed-loop PI feedback that completes the full softmax-exponentiation, summation, and normalization-on a single photonic chip without per-channel normalization circuitry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12934 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Photonic Exponential Approximation via Cascaded TFLN Microring Resonators toward Softmax Park, Hyoseok Park, Yeonsang Optics The rapid growth of large-scale AI models has intensified energy consumption and data-movement challenges in modern datacenters. Photonic accelerators offer a promising path by executing the linear matrix multiplications of transformer inference at high throughput and low energy. However, the softmax attention layer, which requires element-wise exponentiation followed by normalization, still relies on electronic post-processing, creating an electro-optic conversion bottleneck that negates much of the potential photonic advantage. We present a cascaded micro-ring resonator (MRR) architecture that synthesizes the per-channel exponential function required by softmax, e^{x_n - max(x)}, over a finite interval with tunable worst-case relative error. A control signal detunes each ring via an electro-optic mechanism; a weak probe at fixed frequency experiences Lorentzian transmission, and cascading N identical stages yields a multiplicative transfer function whose logarithm is approximately linear. We derive mapping rules, depth-scaling estimates, and a minimax fitting formulation, and validate the framework with three-dimensional FDTD simulations of X-cut thin-film lithium niobate (TFLN) add-drop micro-ring resonators. Direct multi-ring FDTD validation extends to a five-ring cascade and confirms agreement with theory primarily over the upper operating range; deeper cascades and higher quality factors are assessed analytically. The cascade implements the per-channel exponential block, the key missing nonlinearity for photonic softmax. We further present a WDM-parallel chip architecture with closed-loop PI feedback that completes the full softmax-exponentiation, summation, and normalization-on a single photonic chip without per-channel normalization circuitry. |
| title | Photonic Exponential Approximation via Cascaded TFLN Microring Resonators toward Softmax |
| topic | Optics |
| url | https://arxiv.org/abs/2603.12934 |