  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-to-end
models for fully connected (FC), convolutional (CNN), and recurrent (RNN)
neural networks. Along with six real-world 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.
