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Benchmarking the Performance and Energy Efficiency of AI Accelerators for AI Training
معیار عملکرد و بهره وری انرژی شتاب دهنده های هوش مصنوعی برای آموزش هوش مصنوعی-2020 Deep learning has become widely used in complex AI
applications. Yet, training a deep neural network (DNNs) model
requires a considerable amount of calculations, long running time,
and much energy. Nowadays, many-core AI accelerators (e.g.,
GPUs and TPUs) are designed to improve the performance of
AI training. However, processors from different vendors perform
dissimilarly in terms of performance and energy consumption.
To investigate the differences among several popular off-theshelf
processors (i.e., Intel CPU, NVIDIA GPU, AMD GPU, and
Google TPU) in training DNNs, we carry out a comprehensive
empirical study on the performance and energy efficiency of
these processors 1 by benchmarking a representative set of deep
learning workloads, including computation-intensive operations,
classical convolutional neural networks (CNNs), recurrent neural
networks (LSTM), Deep Speech 2, and Transformer. Different
from the existing end-to-end benchmarks which only present the
training time, We try to investigate the impact of hardware,
vendor’s software library, and deep learning framework on
the performance and energy consumption of AI training. Our
evaluation methods and results not only provide an informative
guide for end users to select proper AI accelerators, but also
expose some opportunities for the hardware vendors to improve
their software library. Index Terms: AI Accelerator | Deep Learning | CPU | GPU | TPU | Computation-intensive Operations | Convolution Neural Networks | Recurrent Neural Networks | Transformer | Deep Speech 2 |
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