Year: 2024
Type: dissertation
Abstract: This thesis lies within the field of unconventional computing, and in particular focuses on physical systems whose dynamics can usefully process information.Such platforms may not outperform an electronic processor but may offer lower power consumption and better integration with sensors, and consequently may be useful for edge computing applications.Here, we study photonic systems, leveraging the relative maturity of the field due to the growth of telecommunication-related industries.Specifically, we explore how the wavelength degree of freedom of the light can be exploited to implement frequency multiplexed Artificial Neural Network (ANN) algorithms.Among many kinds of ANN algorithms, we focus on randomized ANNs. These networks are mostly composed of randomly selected and untrained connections, and only the output connections are tuned. Randomized ANNs offer interesting advantages, such as the possibility of low-cost training (both in terms of time and power) and the simplicity of implementation in physical computers. We describe the implementation of three randomized ANN algorithms: Extreme Learning Machine (ELM), Reservoir Computer (RC), and Deep Reservoir Computer (Deep-RC).We describe a variety of schemes where artificial neurons are encoded in the lines of an optical frequency comb.The mixing of information, necessary for machine learning, happens either using periodic phase modulation or using Kerr nonlinearity.Output weights can be applied optically by using programmable spectral filters.Each scheme is experimentally demonstrated in a fiber-based setup and evaluated on standard machine learning benchmark tasks.Benchmark results are in line with the performance reported on similar photonic-based systems and equivalent software implementations. We demonstrate that parallel operations can be executed on a single hardware.Parallel-running computations can be concatenated, thus constituting the layers of a Deep-RC.To the best of our knowledge, we report the first optical Deep-RC where connection among layers is entirely analog.We also demonstrate that an optical frequency comb propagating in a relatively short optical fiber can accumulate enough Kerr nonlinearity to usefully process information.Finally, we describe the design of two integrated photonic circuits that could constitute an integrated optical output layer for our frequency multiplexed systems.The first chip is an integrated programmable spectral filter, faster than the currently employed commercial solution.The second chip is a wavelength converter intended to sum the artificial neuron values encoded on a frequency comb, thus producing a monochromatic optical output.
Author: Lupo, Alessandro
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
Award: G2258313134
OpenAlex ID: W7032921130