  A composable infrastructure is defined as resources, such as compute,
storage, accelerators and networking, that are shared in a pool and that can be
grouped in various configurations to meet application requirements. This
freedom to 'mix and match' resources dynamically allows for experimentation
early in the design cycle, prior to the final architectural design or hardware
implementation of a system. This design provides flexibility to serve a variety
of workloads and provides a dynamic co-design platform that allows experiments
and measurements in a controlled manner. For instance, key performance
bottlenecks can be revealed early on in the experimentation phase thus avoiding
costly and time consuming mistakes. Additionally, various system-level
topologies can be evaluated when experimenting with new System on Chip (SoCs)
and new accelerator types. This paper details the design of an enterprise
composable infrastructure that we have implemented and made available to our
partners in the IBM Research AI Hardware Center (AIHC). Our experimental
evaluations on the composable system give insights into how the system works
and evaluates the impact of various resource aggregations and reconfigurations
on representative deep learning benchmarks.
