Using interpretable ML to co-design storage hardware and firmware together—rather than separately—helps engineers make better architectural decisions by understanding how memory, error handling, and workloads interact.
This paper describes how machine learning can optimize the design of solid-state drives (SSDs) by modeling how error management algorithms interact with memory components under different workloads. The researchers built an interpretable ML framework that analyzes thousands of real SSDs to guide hardware design decisions, enabling better performance and reliability trade-offs.