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 s per prediction), and low energy consumption (19.5 mu J) opens up unprecedented usage models in the storage environment.

Enabling Computational Storage Through FPGA Neural Network Accelerator for Enterprise SSD

Zambelli C.
Primo
;
Zuolo L.;Olivo P.
Ultimo
2019

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 s per prediction), and low energy consumption (19.5 mu J) opens up unprecedented usage models in the storage environment.
2019
Zambelli, C.; Bertaggia, R.; Zuolo, L.; Micheloni, R.; Olivo, P.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2415822
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