PNNL

Abstract

The AMAIS (Advanced Memory to Support AI for Science) program addresses the growing imbalance between computation and memory performance in modern scientific and AI workloads—commonly known as the memory wall. The project introduces an integrated hardware–software co-design approach that leverages fabric-attached memory, near-memory compute, and advanced memory-centric software tools to enable scalable, high-performance execution of data-intensive applications. Key innovations include the Memory Archipelago architecture, the RRAPID software suite for accelerator synthesis and analysis, CXL-based platforms such as Two Sisters and Crete, and OCEAN, an open-source emulation environment that enables hyperscale experimentation with emerging memory fabrics. Through algorithm– architecture co-design, novel HLS methodologies, performance analysis frameworks, distributed runtimes, and new compiler technologies, AMAIS demonstrates how large, heterogeneous memory pools can support next-generation AI, HPC, and converged simulation workflows. This work establishes foundational methods and platforms for exploiting disaggregated memory systems at scale and provides early evidence of the transformative potential of CXL-enabled architectures for scientific computing.