Benefits of Google Cloud Tensor Processing Units (TPUs)

TPUs are best suited if you want to deploy Models dominated by matrix computations with no custom TensorFlow operations inside the main training loop or Models that train for weeks or months or very large Models with very large effective batch sizes.

Introduction of Cloud TPUs

• Google Cloud TPU - It is an AI accelerator application-specific integrated circuit which is developed by Google specifically for Neural Network Machine Learning software.
• Google began using it (TPU software) internally in 2015 as well as in 2018 made them available for third party use, both of its part - cloud infrastructure and smaller version of the machine chip for sale.
• It was announced in May 2016 at Google I/O, when the company confirmed that the Google Cloud TPU had already been used inside their data centers for over a year.

 Advantages of TPUs

• Google Cloud resources of TPUs accelerate the performance of linear algebra computation which is used heavily in machine applications to plan it. 
• It minimizes the time-to-accuracy when you train large and complex neural network models. 
• Models that were previously designed took weeks to train on other hardware platforms, and can converge in hours on TPUs.

Features of Tensor Processing Units 

• Built for AI on Google Cloud - Cloud TPUs is designed to run cutting-edge machine learning models with AI services on Google Cloud and its custom high-speed network offers over 100 petaflops of performance in a single pod.
• Proven, state-of-the-art models - In this phase, you can build your own machine learning-powered solutions for many real-world use cases, download a Google-optimized reference model, just bring your data, & the start training.

Limitations of Cloud TPUs:

• The non-matrix multiplication based workloads are unlikely to perform well on TPUs. 
• If workload requires high-precision arithmetic then cloud TPUs are not the best choice.