LuxIA: A Lightweight Unitary matriX-based Framework Built on an Iterative Algorithm for Photonic Neural Network Training
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Abstract (arXiv)
PNNs present promising opportunities for accelerating machine learning by leveraging the unique benefits of photonic circuits. However, current state of the art PNN simulation tools face significant scalability challenges when training large-scale PNNs, due to the computational demands of transfer matrix calculations, resulting in high memory and time consumption. To overcome these limitations, we introduce the Slicing method, an efficient transfer matrix computation approach compatible with back-propagation. We integrate this method into LuxIA, a unified simulation and training framework. The Slicing method substantially reduces memory usage and execution time, enabling scalable simulation and training of large PNNs. Experimental evaluations across various photonic architectures and standard datasets, including MNIST, Digits, and Olivetti Faces, show that LuxIA consistently surpasses existing tools in speed and scalability. Our results advance the state of the art in PNN simulation, making it feasible to explore and optimize larger, more complex architectures. By addressing key computational bottlenecks, LuxIA facilitates broader adoption and accelerates innovation in AI hardware through photonic technologies. This work paves the way for more efficient and scalable photonic neural network research and development.
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References · 12
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2021Scaling Deep Contrastive Learning Batch Size under Memory Limited Setupcited by 123
- 2018Training of photonic neural networks through in situ backpropagationcited by 45
- 2022AI and ML Accelerator Survey and Trendscited by 23
- 2019Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networkscited by 19
- 2021Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignmentcited by 17
- 2020Modeling Silicon-Photonic Neural Networks under Uncertaintiescited by 7
- 2018Matrix optimization on universal unitary photonic devicescited by 5
- 2019Parallel fault-tolerant programming of an arbitrary feedforward photonic networkcited by 4
- 2024Architecture-Level Modeling of Photonic Deep Neural Network Acceleratorscited by 4
- 2020Simphony: An open-source photonic integrated circuit simulation frameworkcited by 1
- 2023NEUROPULS: NEUROmorphic energy-efficient secure accelerators based on Phase change materials aUgmented siLicon photonicScited by 1
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