Embedded Computing Design

The continued evolution of artificial intelligence (AI), particularly in energy-sensitive edge inference and dense AI server workloads, demands radical innovation in system memory. SOCAMM (Small Outline Compression Attached Memory Module) emerges as a purpose-built memory architecture designed by NVIDIA and developed in partnership with Micron, Samsung, and SK Hynix to bridge a critical performance and efficiency gap in AI-centric platforms. By combining the high-bandwidth and energy efficiency of LPDDR5X memory with a modular, socketed form factor, SOCAMM redefines the interface between memory and compute.

Emerging workloads across AI, big data, IoT, and hyperscale compute require a rethinking of storage infrastructure. As data volume surges, particularly in the “warm data” tier, where information is accessed frequently but not in real-time, existing storage form factors struggle to scale simultaneously in terms of capacity, density, power, and performance.