COmplex media and METasurfaces for scalable and efficient photonic neural networks

With the advent of large language models (e.g. ChatGPT), the use of artificial neural networks has become ubiquitous in our society. However, the energy consumption is currently doubling every two months, leading to an unsustainable growth. This is primarily caused by the extensive interconnectivity that characterizes neural networks’ hardware architecture, which are notably challenging and power-consuming to implement in electronics. Photonics offers a promising solution, enabling faster operations with significantly reduced energy consumption. Fully-connected layers are already available in several photonic platforms, including complex media, one of the applicant’s main areas of expertise. However, optics still lacks a fundamental building block for implementing neural networks with advanced learning capabilities: a nonlinear activation layer that is non-polynomial and able to cascade its signal to subsequent layers. This MSCA PF will focus on developing a thermo-optically reconfigurable nonlinear metasurface, designed to implement such nonlinear activation layer. With this innovative device, the COMET project will demonstrate that the combination of COmplex media and METasurfaces can provide a scalable, computationally powerful and efficient photonic neural network.The project will be conducted at Politecnico di Milano with Prof. Michele Celebrano, expert in nonlinear optics and metasurfaces. It will also include a secondment at Sorbonne Université, in Prof. Sylvain Gigan’s group, which specializes in complex media and their applications in photonic computing. The combined expertise of the applicant and the two host institutions will ensure that CoMet makes its unique contribution to the development of more efficient next-generation AI.The scientific excellence of CoMet, along with the experience gained during the PF, will greatly improve my chances of obtaining an ERC Starting Grant, or secure a tenure-track position to establish my own research group.