Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonics

In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) and graph processing have emerged as transformative technologies for natural language processing (NLP), computer vision, and graph-structured data applications. However, the complex structures of these models pose challenges for acceleration on conventional electronic platforms. In this paper, we describe novel hardware accelerators based on silicon photonics to accelerate transformer neural networks that are used in LLMs and graph neural networks for graph data processing. Our analysis demonstrates that both hardware accelerators achieve at least 10.2x throughput improvement and 3.8x better energy efficiency over multiple state-of-the-art electronic hardware accelerators designed for LLMs and graph processing

PABED A Tool for Big Education Data Analysis

Cloud computing and big data have risen to become the most popular technologies of the modern world. Apparently, the reason behind their immense popularity is their wide range of applicability as far as the areas of interest are concerned. Education and research remain one of the most obvious and befitting application areas. This research paper introduces a big data analytics tool, PABED Project Analyzing Big Education Data, for the education sector that makes use of cloud-based technologies. This tool is implemented using Google BigQuery and R programming language and allows comparison of undergraduate enrollment data for different academic years. Although, there are many proposed applications of big data in education, there is a lack of tools that can actualize the concept into practice. PABED is an effort in this direction. The implementation and testing details of the project have been described in this paper. This tool validates the use of cloud computing and big data technologies in education and shall head start development of more sophisticated educational intelligence tools

The Evidence Hub: harnessing the collective intelligence of communities to build evidence-based knowledge

Conventional document and discussion websites provide users with no help in assessing the quality or quantity of evidence behind any given idea. Besides, the very meaning of what evidence is may not be unequivocally defined within a community, and may require deep understanding, common ground and debate. An Evidence Hub is a tool to pool the community collective intelligence on what is evidence for an idea. It provides an infrastructure for debating and building evidence-based knowledge and practice. An Evidence Hub is best thought of as a filter onto other websites — a map that distills the most important issues, ideas and evidence from the noise by making clear why ideas and web resources may be worth further investigation. This paper describes the Evidence Hub concept and rationale, the breath of user engagement and the evolution of specific features, derived from our work with different community groups in the healthcare and educational sector