Experimental reservoir computing with diffractively coupled VCSELs
arXiv:2412.03206 · doi:10.1364/OL.518946
Abstract
We present experiments on reservoir computing (RC) using a network of vertical-cavity surface-emitting lasers (VCSELs) that we diffractively couple via an external cavity. Our optical reservoir computer consists of 24 physical VCSEL nodes. We evaluate the system's memory and solve the 2-bit XOR task and the 3-bit header recognition (HR) task with bit error ratios (BERs) below 1\,\% and the 2-bit digital-to-analog conversion (DAC) task with a root-mean-square error (RMSE) of 0.067.
References in corpus (7)
- A survey of cross-validation procedures for model selection
- All-Optical Machine Learning Using Diffractive Deep Neural Networks
- Reinforcement Learning in a large scale photonic Recurrent Neural Network
- Deep Learning with Coherent VCSEL Neural Networks
- Developing of a photonic hardware platform for brain-inspired computing based on VCSEL arrays
- Diffractive coupling for photonic networks: how big can we go?
- Injection locking and coupling the emitters of large VCSEL arrays via diffraction in an external cavity