Artificial intelligence (AI), machine learning, and edge computing are driving unprecedented demand for high-performance, energy-efficient computing hardware, while electronic accelerators face limitations in speed, scalability, and energy consumption as transistor scaling slows down. Neuromorphic photonics has emerged as a compelling approach to overcome these barriers by harnessing the inherent parallelism, high bandwidth, and ultrafast response of light.

Recent advances in silicon photonic devices, heterogeneous integration, and novel optical materials such as barium titanate (BTO) and two-dimensional materials (e.g., MoS₂, graphene) are enabling compact, CMOS-compatible photonic computing systems that deliver high-speed, low-latency, and energy-efficient information processing, supporting next-generation capabilities in AI acceleration, signal processing, sensing, and LIDAR.

This workshop aims to bring together experts from academia, research institutes, and industry to explore challenges, technological enablers, and future directions in neuromorphic photonics, while also identifying opportunities for deeper cooperation in next-generation computing and communication systems.

Key topics include emerging photonic device technologies, MEMS and phase-change devices, heterogeneous integration of InP with silicon photonics, novel materials, on-chip photonic interconnects, photonic neural network architectures, AI accelerators, photonic LIDAR, signal processing systems, and cross-layer optimization between algorithms, hardware, and photonic circuits.

The workshop is organised by Miltiadis Moralis-Pegios (AUTH, Greece), Kyoungsik Yu (KAIST, Republic of Korea), and Ruud Oldenbeuving (IMEC, Netherlands).