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

arXiv: https://arxiv.org/abs/2006.01475 · HTML: https://mlsanity.com/paper/2006.01475/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Scaling Laws Beyond Backpropagation](https://mlsanity.com/paper/2210.14593/) (2022)

## Abstract (arXiv)

As neural networks grow larger and more complex and data-hungry, training costs are skyrocketing. Especially when lifelong learning is necessary, such as in recommender systems or self-driving cars, this might soon become unsustainable. In this study, we present the first optical co-processor able to accelerate the training phase of digitally-implemented neural networks. We rely on direct feedback alignment as an alternative to backpropagation, and perform the error projection step optically. Leveraging the optical random projections delivered by our co-processor, we demonstrate its use to train a neural network for handwritten digits recognition.