A scalable and fully tuneable VCSEL-based neural network
oleh: Skalli Anas, Goldmann Mirko, Porte Xavier, Haghighi Nasibeh, Reitzenstein Stephan, Lott James A., Brunner Daniel
| Format: | Article |
|---|---|
| Diterbitkan: | EDP Sciences 2023-01-01 |
Deskripsi
We experimentally demonstrate an autonomous, fully tuneable and scalable neural network of 350+ parallel nodes based on a large area, multimode semiconductor laser. We implement online learning strategies based on reinforcement learning. Our system achieves high performance and a high classification bandwidth of 15KHz for the MNIST dataset. Our approach is highly scalable both in terms of classification bandwidth and neural network size.