CAMEL: 컴퓨터 아키텍처 및 메모리 시스템 연구실

Modern AI services move large models, embeddings, KV caches, checkpoints, and graph data through memory and storage hierarchies. We build computational memory and storage systems that place data preparation and processing close to the data. Current work includes in-storage RAG acceleration, host-free checkpointing for multimodal LLM training, hardware-driven GNN preprocessing, ML-aware SSD reliability, and CXL-enabled storage disaggregation. Our goal is to reduce data movement while improving end-to-end performance, energy efficiency, and reliability. We turn emerging-memory concepts into deployable hardware/software systems. Our research prototypes combine computational-memory SoCs, chiplet-based components, FPGA/RTL controllers, near-data processing datapaths, and Linux-compatible software stacks. We validate these systems with LLM, RAG, recommendation, graph, and datacenter workloads so architectural ideas can be measured on real platforms rather than only through simulation. Datacenter SSD behavior depends on firmware, controllers, operating-system policies, and workload characteristics. We design hardware/software mechanisms for predictable performance, reliability, endurance, thermal management, I/O scheduling, garbage collection, and in-storage processing. Recent work includes ML-aware reliability hints, next-generation SSD arrays, ZNS all-flash arrays, and containerized computational storage. Memory capacity, communication, and data movement increasingly limit AI and HPC workloads. We study memory-centric system architectures that combine disaggregated memory, computational storage, checkpointing, and near-data acceleration to improve performance and energy efficiency. Our designs target scientific computing as well as modern LLM, graph, and recommendation workl