Photonic enabled Petascale in-memory computing with Femtojoule energy consumption (PHOENICS)

Basic data for this project

Type of project: EU-project hosted at University of Münster
Duration: 01/01/2021 - 31/12/2024

Description

Modern societies and economies increasingly depend on the massive generation of data resulting from the exponential growth of internet applications. Drastically enhanced computational performance is in particular needed for a plethora of applications in artificial intelligence (AI) which necessitate unprecedented processing power, memory and communication bandwidth. This demand cannot be met by modern digital electronic technologies that are rapidly approaching their physical limits. The PHOENICS consortium will break through these barriers and lay the foundation for a disruptive neuromorphic compute platform based on hybrid photonic integrated circuits. By providing access to parallelized neuromorphic processing using wavelength division multiplexing, the PHOENICS consortium will harness exceptional scaling potential not available to electronic systems and will deliver multiply-accumulate (MAC) performance at 3.2 PetaMAC/s at an energy cost of 50 FemtoJoule/MAC. Building on a hybrid architecture with substantial potential for future upscaling, the PHOENICS project aims at implementing a disruptive architecture which outperforms state-of-the-art electronic neuromorphic hardware. The consortium partners have shown the significant technological potential that a photonic approach can offer by establishing a new brain-inspired computing paradigm using phase-change-materials. By implementing scalable systems based on foundry processing for creating bio-mimicking material platforms, the PHOENICS consortium will provide a new generation of photonic hardware accelerators for neuromorphic processing and develop a strong ecosystem for photonic computing. The PHOENICS technology will thereby directly impact today's technology and likewise address future needs for high bandwidth and low latency compute systems for AI.

Keywords: Computer science; Information science; Artificial intelligence; AI