Enabling Computational Storage Through FPGA Neural Network Accelerator for Enterprise SSD
| Autor: | Cristian Zambelli, Lorenzo Zuolo, Piero Olivo, Rino Micheloni, Riccardo Bertaggia |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
accelerator
Computer science neural network Reliability (computer networking) Big data 02 engineering and technology NO Gate array solid state drives (SSD) 020204 information systems 0202 electrical engineering electronic engineering information engineering PE7_2 PE7_5 Electrical and Electronic Engineering Latency (engineering) accelerator Computational storage FPGA NAND flash neural network solid state drives (SSD) Field-programmable gate array FPGA Interconnection Hardware_MEMORYSTRUCTURES Computational storage business.industry 020208 electrical & electronic engineering Process (computing) NAND flash Embedded system business Host (network) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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