Reservoir Photonic Computing Overview
Introduction
AI & ML rides on the fast expansion of computing power. Ultimately, it is the data processing speed and capacity that limit how “intelligent” an AI machine can be.
That’s why we build super-AI systems using hybrid photonic-electronic architectures where photons and electrons work together to push the computing boundaries needed for AI & ML.
Photonic computing can be extremely fast and energy efficient, and inherently supports parallel processing at large scale. Yet it is challenging to prepare, interact, and store many photonic signals. Conversely, electronic signals are easy to create and manipulate, but their processing speed and parallelism are heavily capped.
We decided to work on hybrid systems combine the benefits of photonics and electronics for computing, while avoiding each’s shortfalls. On top of their exceeding processing speed and data capacity, advanced algorithms are developed to unleash unprecedented AI/ML capabilities.
What is photonic reservoir computing?
Reservoir computing is a framework for computation derived from recurrent neural network theory, which maps input signals into higher dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir. After the input signal is fed into the reservoir, which is treated as a "black box," a simple readout mechanism is trained to read the state of the reservoir and convert it to the desired output.
The first key benefit of this framework is that training is performed only at the readout stage, as the reservoir dynamics are fixed.
The second is that the computational power of naturally available systems, both classical and quantum mechanical, can be conveniently utilized to reduce the effective computational cost. It is this advantage and its success on lots of time-dependent tasks, such as chaotic time series prediction, radar signal classification, and speech recognition.
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See our list of publications for a deep dive into photonic reservoir computing.