Next Gen. Memory & Storage Solution

Computing systems' memory and storage requirements include latency, bandwidth, and capacity. The latest systems are focusing more on securing high bandwidth than on improving latency, and HBM is a good example. Also, as the scale of data becomes increasingly important, large memory capacity is also important. Furthermore, developing solutions which meet diverse AI workload requirements at low cost is becoming crucial.

Faster Memory Solution

  • HBM theoretically provides a very high bandwidth, but its performance might be limited depending on the access pattern of applications. Therefore, we are researching how to integrate various acceleration technologies into the base die of HBM. We are exploring various technologies suggested by academia and industry to select the most efficient technology that works within the limited space and power budget.
  • As AI applications become more widespread, we are also responding to new demands which require more than CPU or GPU-based solutions. Next-generation acceleration devices and solutions such as PIM, PNM, and Computational Storage are more energy efficient compared to the existing CPUs and GPUs, since they provide higher data access efficiency.

New Memory Hierarchy

  • In data-centric era, it is challenging to address memory capacity requirement with HBM alone, so we are studying CXL-based disaggregated memory, DRAM+NAND hybrid memory, and next-generation AI storage solutions for GPUs. Since cost efficiency is critical for these new memory and storage tiers, we are doing research on various technologies to maximize cost efficiency while minimizing performance degradation.
  • As these new memory and storage tiers are structurally different from traditional memory/storage, system software like OS and Hypervisor also face new challenges. Therefore, we are actively participating in open source system software development in cooperation with Linux community.

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