Massively Scalable Wavelength Diverse Integrated Photonic Linear Neuron
Matthew van Niekerk, Anthony Rizzo, Hector Rubio Rivera, Gerald Leake,, Daniel Coleman, Christopher Tison, Michael Fanto, Keren Bergman, Stefan, Preble

TL;DR
This paper presents a new scalable silicon photonic neural network architecture supporting both coherent and incoherent operations, demonstrated through on-chip logical operations and high-accuracy 2-bit gate predictions, promising for large-scale neuromorphic photonics.
Contribution
Introduces a novel wavelength-diverse integrated photonic neural network architecture supporting both operation modes with reduced footprint and demonstrated on-chip logical functions and gate predictions.
Findings
Successfully demonstrated on-chip addition and subtraction operations.
Achieved high accuracy in predicting 2-bit logical gates.
Enabled massively scalable photonic neural networks with wavelength parallel sources.
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 (CMOS) 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…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Photonic and Optical Devices · Optical Network Technologies
