Neng Liu | Searcharxiv

arXiv2024

Next generation Co-Packaged Optics Technology to Train & Run Generative AI Models in Data Centers and Other Computing Applications

We report on the successful design and fabrication of optical modules using a 50 micron pitch polymer waveguide interface, integrated for low loss, high density optical data transfer with very low space requirements on a Si photonics die. This prototype module meets JEDEC reliability standards and promises to increase the number of optical fibers that can be connected at the edge of a chip, a measure known as beachfront density, by six times compared to state of the art technology. Scalability of the polymer waveguide to less than 20 micron pitch stands to improve the bandwidth density upwards of 10 Tbps/mm.

physics.optics↗

arXiv2023

Rule-Guided Joint Embedding Learning over Knowledge Graphs

Recent studies on knowledge graph embedding focus on mapping entities and relations into low-dimensional vector spaces. While most existing models primarily exploit structural information, knowledge graphs also contain rich contextual and textual information that can enhance embedding effectiveness. In this work, we propose a novel model that integrates both contextual and textual signals into entity and relation embeddings through a graph convolutional network. To better utilize context, we introduce two metrics: confidence, computed via a rule-based method, and relatedness, derived from textual representations. These metrics enable more precise weighting of contextual information during embedding learning. Extensive experiments on two widely used benchmark datasets demonstrate the effectiveness of our approach, showing consistent improvements over strong baselines.

cs.CL↗