Large-scale neural network in passive silicon photonics for biologically plausible learning

Alessio Lugnan; Alessandro Foradori; Stefano Biasi; Peter Bienstman; Lorenzo Pavesi

doi:10.1117/12.3017123

18 June 2024 Large-scale neural network in passive silicon photonics for biologically plausible learning

Alessio Lugnan, Alessandro Foradori, Stefano Biasi, Peter Bienstman, Lorenzo Pavesi

Proceedings Volume 13017, Machine Learning in Photonics; 130170S (2024) https://doi.org/10.1117/12.3017123
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Neuromorphic computing hardware that requires conventional training procedures based on backpropagation is difficult to scale, because of the need for full observability of network states and for programmability of network parameters. Therefore, the search for hardware-friendly and biologically-plausible learning schemes, and suitable platforms, is pivotal for the future developments of the field. We present a novel experimental study of a photonic integrated neural network featuring rich recurrent nonlinear dynamics and both short- and long-term plasticity. Scalability in these architectures is greatly enhanced by the capability to process input and to generate output that are encoded concurrently in the temporal, spatial and wavelength domains. Moreover, we discuss a novel biologically-plausible, backpropagation-free and hardware-friendly learning procedure based on our neuromorphic hardware.

Conference Presentation

Citation Download Citation

Alessio Lugnan, Alessandro Foradori, Stefano Biasi, Peter Bienstman, and Lorenzo Pavesi "Large-scale neural network in passive silicon photonics for biologically plausible learning", Proc. SPIE 13017, Machine Learning in Photonics, 130170S (18 June 2024); https://doi.org/10.1117/12.3017123

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available