Because training deep learning models requires intensive computation, AI researchers are always on the lookout for new and better hardware and software platforms for their increasingly sophisticated models. Which hardware platforms -- TPU, GPU or CPU -- are best suited for training deep learning models has been a matter of discussion in the AI community for years. A new Harvard University study proposes a benchmark suite to analyze the pros and cons of each. "ParaDnn" is the product of a group of Harvard University researchers at the School of Engineering and Applied Sciences. The parameterized benchmark suite conducts subjective comparisons between different computing platforms for deep learning models.