Computer Architecture and Systems Lab (CASL): casl.yonsei.ac.kr

The word cloud image below was generated from the abstracts of our papers published since 2023. This can provide a quick snapshot of our lab’s research interests. The ongoing evolution of computing systems is fundamentally reshaping the role of memory. Traditional CPU-centric architectures built around homogeneous commodity DRAM have given way to heterogeneous platforms that integrate GPUs, domain-specific accelerators, and emerging memory technologies such as processing in/near memory. At the same time, memory hierarchies are expanding into multiple tiers, spanning on-package DRAM and disaggregated memory via CXL. These changes challenge the long-standing assumptions that underpin conventional memory management methods. In this new landscape, existing memory management and virtualization mechanisms designed for traditional host memory have become a critical bottleneck, introducing inefficiencies, constraining scalability, and obscuring the performance potential of future computing systems. While we envision that memory management must ultimately be redesigned to support modern computing environments, our research pursues a pragmatic path forward by identifying and resolving inefficiencies in current system-level memory management, paving the way toward more adaptive, efficient, and scalable memory architectures. Large language models (LLMs) are rapidly redefining the performance requirements of computing systems, with inference tasks emerging as dominant and cost-critical workloads. A central bottleneck in LLM execution is the key-value (KV) cache used by attention mechanisms, which grows linearly with sequence length and model size, placing immense pressure on memory capacity and bandwidth. As LLMs scale toward long-context and multi-tenant settings, simply storing an