Efficient General Intelligence with Novel Model and Customized Silicon Co-Design, Jason Cong (UCLA)

Distinguished Lecture by Prof. Jason Cong at the Special Joint Engineering and AI Seminar at Brown University on April 24, 2026. Prof. Cong discusses the opportunities and progress for AI models and hardware accelerator co-design towards the goal of efficient general intelligence (EGI). Some successful examples include hierarchical memory transformer (HMT), lookup-table (LUT) based large-language models (LLMs), and customized acceleration of the LLM memory processing pipeline. He also presents the latest research results at UCLA on using AI/ML techniques, such as graph neural networks (GNNs), LLMs, and agentic approaches, coupled with algorithmic methods, such as high-level synthesis and non-linear programming, to automate chip designs to enable rapid design of deep learning models on customized silicon.