Far-memory systems, where applications store less-active data in more energy-efficient memory media, are increasingly adopted by data centers. However, applications are bottlenecked by on-demand data fetching from far- to local-memory. We present 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%.

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