Training deep learning models is compute-intensive and there is an industry-wide trend towards hardware specialization to improve performance. To systematically benchmark deep learning platforms, we introduce ParaDnn, a parameterized benchmark suite for deep learning that generates end-toend models for fully connected (FC), convolutional (CNN), and recurrent (RNN) neural networks. Along with six realworld models, we benchmark Google's Cloud TPU v2/v3, NVIDIA's V100 GPU, and an Intel Skylake CPU platform. We take a deep dive into TPU architecture, reveal its bottlenecks, and highlight valuable lessons learned for future specialized system design. We also provide a thorough comparison of the platforms and find that each has unique strengths for some types of models. Finally, we quantify the rapid performance improvements that specialized software stacks provide for the TPU and GPU platforms.
Wang et al. REF proposed a parameterized benchmark suite, ParaDNN, to characterize TPU, GPU, and CPU for training DNN models (including FC, CNN, and RNN).
Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
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