Massively scalable wavelength diverse integrated photonic linear neuron
Abstract
As computing resource demands continue to escalate in the face of big data, cloud-connectivity and the internet of things, it has become imperative to develop new low-power, scalable architectures. Neuromorphic photonics, or photonic neural networks, have become a feasible solution for the physical implementation of efficient algorithms directly on-chip. This application is primarily due to the linear nature of light and the scalability of silicon photonics, specifically leveraging the wide-scale complementary metal-oxide-semiconductor manufacturing infrastructure used to fabricate microelectronics chips. Current neuromorphic photonic implementations stem from two paradigms: wavelength coherent and incoherent. Here, we introduce a novel architecture that supports coherent and incoherent operation to increase the capability and capacity of photonic neural networks with a dramatic reduction in footprint compared to previous demonstrations. As a proof-of-principle, we experimentally demonstrate simple addition and subtraction operations on a foundry-fabricated silicon photonic chip. Additionally, we experimentally validate an on-chip network to predict the logical 2 bit gates AND, OR, and XOR to accuracies of 96.8%, 99%, and 98.5%, respectively. This architecture is compatible with highly wavelength parallel sources, enabling massively scalable photonic neural networks.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 01, 2022
- Source ID
- 10.1088/2634-4386/ac8ecc
Entities
People
- Anthony Rizzo
- Christopher Tison
- Daniel Coleman
- Gerald Leake
- Hector Rubio
- Keren Bergman
- Matthew van Niekerk
- Michael Fanto
- Stefan F. Preble
Organizations
- Air Force Research Laboratory
- National Science Foundation