• Technical Conference:  24 – 28 March 2024
  • Exhibition: 26 – 28 March 2024
  • San Diego Convention Center, San Diego, California, USA

Roadmap for Photonic AI Accelerators

Thursday, 09 March, 14:00 – 16:00

Room 7AB


In addition to the known high-bandwidth benefit, photonics offers two key functionalities with relevance to AI accelerators for machine learning, namely, multiplication and accumulation such as enabled via, for example, modulators and photodetectors, respectively. However, photonics is challenged to provide end-to-end neural network solutions reflected by the challenge of a nonlinear activation function leading to OEO conversions. This channels realisations of deep neural network architectures in the optical domain requiring electronics introducing parasitic conversions. In addition optical accelerators being analog compute engines may require (depending on the application) digital-to-analog domain crossing which is expensive. 

In this panel we will review the state-of-the-art in photonic AI accelerators and will project challenges and solutions into the future for photonic and hybrid accelerators for AI and machine intelligence. Here our perspective is open to application spaces in network edge AI and to machine learning training in the cloud.


Glenn Bartolini, Coherent Corp, USA

Nikos Pleros, Aristotle University of Thessaloniki, Greece

Volker Sorger, George Washington University, USA

Xian Xiao, Hewlett Packard Labs, USA


Darius Bunandar, Lightmatter, USA

Hamed Dalir, Optelligence LLC , USA

Johannes Feldmann, Salience Labs, UnitedKingdom

Michael Hochberg, Luminous, USA

Bahram Jalali, UCLA, USA

Francesca Parmigianni, Microsoft Research Cambridge, UnitedKingdom