Computing for Embedded Lightweight Learning (CELL)

Our research focuses on the development of state-of-the-art generative AI models, including Large Language Models (LLMs) and Diffusion models, as well as the exploration of foundational theoretical methods. We apply these advanced techniques to emerging fields (e.g. system optimization, performance evaluation), aiming to transcend the limitations of traditional approaches and to propose innovative designs for contemporary systems. Additionally, our approach explores the potential of LLMs to address a variety of tasks. Specifically, we target the development of acceleration techniques, such as knowledge compression and learning strategies, to optimize and lightweight LLM structures for more efficient deployment. Hyperdimensional (HD) computing is an alternative computing method that processes cognitive tasks in a lightweight and error-torrent way based on theoretical neuroscience. The mathematical properties of high-dimensional spaces have remarkable agreements with brain behaviors. Thereby, HD computing emulates human cognition by computing with high-dimensional vectors, called hypervectors, as an alternative to traditional computing numbers like integers and booleans. With concrete hypervector arithmetics, we enable various pattern-based computations such as memorizing and reasoning, similar to what humans are doing. Our group works on developing diverse learning and cognitive computing techniques based on HD computing, focusing on typical ML tasks, neuro-symbolic AI, and acceleration in next-generation computing environments. Machine Learning (ML) is increasingly recognized as a pivotal technology for autonomous data analysis and pattern recognition. Our research group is at the forefront of redefining the role of ML in system design, focusing on innovative solutions