🤖 AI Summary
This work proposes an interpretable machine learning–based cross-layer co-design methodology to address the challenge of jointly optimizing reliability and performance in high-density solid-state storage systems. By constructing the first representation learning framework that unifies NAND flash error management with diverse real-world and synthetic workloads—including JEDEC and YCSB benchmarks—and integrating it with an abstracted Flash Translation Layer model, the study systematically analyzes the interaction mechanisms between memory components and firmware algorithms. Extensive experiments across thousands of multi-generation datacenter SSDs demonstrate that the proposed approach significantly enhances storage architecture design efficiency, enables data-driven continuous evolution, and achieves synergistic optimization of both reliability and performance under realistic and synthetic workloads.
📝 Abstract
Solid-state storage architectures based on NAND or emerging memory devices (SSD), are fundamentally architected and optimized for both reliability and performance. Achieving these simultaneous goals requires co-design of memory components with firmware-architected Error Management (EM) algorithms for density- and performance-scaled memory technologies. We describe a Machine Learning (ML) for systems methodology and modeling for co-designing the EM subsystem together with the natural variance inherent to scaled silicon process of memory components underlying SSD technology. The modeling analyzes NAND memory components and EM algorithms interacting with comprehensive suite of synthetic (stress-focused and JEDEC) and emulation (YCSB and similar) workloads across Flash Translation abstraction layers, by leveraging a statistically interpretable and intuitively explainable ML algorithm. The generalizable co-design framework evaluates several thousand datacenter SSDs spanning multiple generations of memory and storage technology. Consequently, the modeling framework enables continuous, holistic, data-driven design towards generational architectural advancements. We additionally demonstrate that the framework enables Representation Learning of the EM-workload domain for enhancement of the architectural design-space across broad spectrum of workloads.