| Autor: |
Vladimir Golubenko, Alexander Suvorov, Yury Gorbachev, Ryan Palmer, Artyom Tugaryov, Andrey Kashchikhin, Mikhail Fedorov, Alina Alborova, Yaroslav Tarkan, Marat Fatekhov, Galina Dedyukhina, Alexander Demidovskij, Igor Salnikov |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
ICTAI |
| DOI: |
10.1109/ictai50040.2020.00106 |
| Popis: |
Dramatic advances in the field of deep learning have led to superhuman results of neural models on various specialized tasks. There is an urgent need in platforms that provide performance tuning, analysis, and deployment capabilities of these models. Several pioneering platforms that have been emerging for the last five years are thoroughly analyzed and compared across numerous criteria. The OpenVINO Deep Learning Workbench (DL Workbench) is a tool designed to improve the usability and workflows for neural network performance, optimization and deployment. DL Workbench tends to play one of leading roles in terms of feature completeness across other existing platforms in the field. It provides unique capabilities of model optimization and deployment to the target hardware. By applying developer experience (DX) insights to the design of innovative deep learning solutions such as the DL Workbench, a high degree of usability can be achieved to support the complex tasks of developing highly optimized deep learning solutions for target hardware. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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