.png){kind=icon}
.png){kind=icon}
Return
Benchmarking Deep Neural Network Inference Performance on Serverless Environments With MLPerf
.png){kind=icon}
PRE
AI
DOI：10.1109/MS.2020.3030199.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We provide a novel decomposition methodology from the current MLPerf benchmark to the serverless function execution model. We have tested our approach in Amazon Lambda to benchmark the processing capabilities of OpenCV and OpenVINO inference engines.
Keywords:
Benchmark testing
FAA
Task analysis
Engines
Computer architecture
Throughput
Computational modeling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
3.2K
Citations:
3.6K