Deep-Learning-Driven Prefetching for Far Memory

Far-memory systems, where applications store less-active data in more energy-efficient memory media, are increasingly adopted by datacenters.However, applications are bottlenecked by on-demand data fetching from far- to local-memory.We present $\textbf{\textit{Memix}}$,a far-memory system that embodies a deep learning–system co-design for efficient and accurate prefetching, minimizing on-demand far-memory accesses.One key observation is that memory accesses are shaped by both application semantics and runtime context, providing an opportunity to optimize each independently.Preliminary evaluation of Memix on data-intensive workloads shows that it outperforms the state-of-the-art far-memory system by up to 42%.