The presenter is a Software Architect at SK hynix, currently serving as Team Leader of the AI Memory Solution System team. He leads research and development in the fields of AI Data Center Solutions and New Memory Technologies, focusing on next-generation memory architectures and AI-optimized system integration.
Due to the rapid scaling of AI models and the explosive growth of inference workloads, memory systems are reaching their limits in terms of capacity and cost. While HBM (High Bandwidth Memory) excels in ultra-high-speed data processing, its limited capacity has become a bottleneck in building large-scale AI infrastructure. This presentation will introduce HBF (High Bandwidth Flash), a next-generation memory technology designed to bridge this gap.I will explain HBF’s performance, capacity, and fundamental operational principles, along with its technical characteristics optimized for LLM inference. Additionally, I will discuss the features of AI applications best suited for HBF and present relevant use cases.Through these insights, I will aim to demonstrate how HBF contributes to the scalability of data centers and provide an outlook on the future development direction of HBF.