Enabling Computational Storage Through FPGA Neural Network Accelerator for Enterprise SSD

Abstract

Computational storage is an emerging concept in big data scenario where the demand to process ever-growing storage workloads is outpacing traditional compute server architectures. To enable this paradigm there is a call for developing accelerators that off-load some of the management routines that are usually demanded to the smartness inside the storage. For enterprise solid-state drives (SSD) this translates into a dedicated hardware that exploits the interconnection fabric of the host with the goal of improving SSD reliability/performance. In this brief, we have developed an field-programmable gate array-based neural network accelerator for the moving read reference shift prediction in enterprise SSD. The accelerator high prediction accuracy (up to 99.5%), low latency (6.5 $\mu \text{s}$ per prediction), and low energy consumption (19.5 $\mu \text{J}$ ) opens up unprecedented usage models in the storage environment.