Silicon photonics has emerged as a promising solution to realize high-performance computing (HPC) systems required in the Big Data era. Having various applications in the domains of HPC, data centers, sensors and bio-sensing, aerospace, artificial intelligence (AI), etc., it has attracted researchers from academia and industries in different fields to explore various benefits and challenges of this technology. As an emerging area, it demands multidisciplinary collaborations and contributions, from material science and engineering, for realizing low-loss CMOS compatible components, to novel system architectures and software CAD and design tools to explore the design space of the resulting complex devices and systems.

The North American Workshop on Silicon Photonics for High-Performance Computing (SPHPC) will bring together experts in Silicon Photonics and in High-Performance Computing (HPC architects and experts, interconnect architects, HPC systems modeling, etc.) to discuss the state-of-the-art with Silicon Photonics based HPC interconnects and computing platforms (e.g., AI accelerators), and the main challenges that must be addressed to accelerate their development. It will consist of invited talks of the highest caliber from academia, industry, and government agencies as well as from different related disciplines. For SPHPC’23, we will focus on a special theme on Photonics for AI and AI for Photonics. In addition, SPHPC will host a student presentation session to invite students working in this area to present their projects at the workshop and receive feedback on their work. This is the event for meeting professionals in the field as well as exchanging and exploring new ideas.

Invited Speakers

We are excited and delighted to welcome the following colleagues at SPHPC:

Ajay Joshi, Boston University, USA	Photonic Computing Architectures for AI: A Systems Perspective
Bhavin Shastri, Queens University, Canada	Neuromorphic Silicon Photonics and Applications from Classical to Quantum
Dan-Xia Xu, National Research Council (NRC), Canada	Optimization in the Non-Convex Design Space of Nanophotonic Components: Mitigation Strategies Using Machine Learning
David Pan, The University of Texas at Austin, USA	Closing the Virtuous Cycle of Photonics for AI and AI for Photonics
Dusan Gostimirovic, McGill University, Canada	Fabrication-Aware Design of Integrated Photonic Devices Using Convolutional Neural Networks
Emanuel Peinke, 3e8, Canada	A Scalable Optics Computing Approach
Nathan Youngblood, University of Pittsburgh, USA	Photonic Architectures for Computing In Memory Using Nonvolatile Optical Materials
Sean Pang, University of Central Florida, USA	Coherent Matrix Accelerator for Scalable Photonic Information Processing
Sebastien Le Beux, Concordia University, Canada	A Nanophotonic Interconnect based on Non-Volatile Phase Change Material
Sudeep Pasricha, Colorado State University, USA	Cross-Layer Design of Machine Learning Accelerators with Silicon Photonics
Volker J. Sorger, George Washington University, USA	Photonic Machine Intelligence: Tensor Core, Convolution Accelerator, Chip Packaging

We appreciate the support of our sponsors for SPHPC’23: