Light-in-the-loop: using a photonics co-processor for scalable training of neural networks

Photonic co-processors in HPC: using LightOn OPUs for Randomized Numerical Linear Algebra

Randomized Numerical Linear Algebra (RandNLA) is a powerful class of methods, widely used in High Performance Computing (HPC). RandNLA provides approximate solutions to linear algebra functions applied to large signals, at reduced computational costs. However, the randomization step for dimensionality reduction may itself become the computational bottleneck on traditional hardware. Leveraging near constant-time linear random projections delivered by LightOn Optical Processing Units we show that randomization can be significantly accelerated, at negligible precision loss, in a wide range of imp…

可编程光子酉处理器实现参数化可微长距离空分复用传输

Programmable Photonic Unitary Processor Enables Parametrized Differentiable Long-Haul Spatial Division Multiplexed Transmission

本文提出参数化空分复用传输，利用可编程光子酉处理器在中继节点直接优化信道，减少数字后处理负载，并通过可微模型和梯度算法实现1300公里三模光纤实验验证。

EEG Opto-processor: epileptic seizure detection using diffractive photonic computing units

Electroencephalography (EEG) analysis extracts critical information from brain signals, which has provided fundamental support for various applications, including brain-disease diagnosis and brain-computer interface. However, the real-time processing of large-scale EEG signals at high energy efficiency has placed great challenges for electronic processors on edge computing devices. Here, we propose the EEG opto-processor based on diffractive photonic computing units (DPUs) to effectively process the extracranial and intracranial EEG signals and perform epileptic seizure detection. The signals …

Hardware Beyond Backpropagation: a Photonic Co-Processor for Direct Feedback Alignment

The scaling hypothesis motivates the expansion of models past trillions of parameters as a path towards better performance. Recent significant developments, such as GPT-3, have been driven by this conjecture. However, as models scale-up, training them efficiently with backpropagation becomes difficult. Because model, pipeline, and data parallelism distribute parameters and gradients over compute nodes, communication is challenging to orchestrate: this is a bottleneck to further scaling. In this work, we argue that alternative training methods can mitigate these issues, and can inform the desig…

ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients

Robustness to adversarial attacks is typically obtained through expensive adversarial training with Projected Gradient Descent. Here we introduce ROPUST, a remarkably simple and efficient method to leverage robust pre-trained models and further increase their robustness, at no cost in natural accuracy. Our technique relies on the use of an Optical Processing Unit (OPU), a photonic co-processor, and a fine-tuning step performed with Direct Feedback Alignment, a synthetic gradient training scheme. We test our method on nine different models against four attacks in RobustBench, consistently impro…

量子光子处理器及更广泛场景的学习理论

A learning theory for quantum photonic processors and beyond

本研究针对连续变量量子电路的量子态、测量与信道学习任务，通过计算伪维度和覆盖数界建立高效可学习性保证，证明其样本复杂度随模式数多项式增长且不随电路深度增长。