Reviewing Google Cloud TPU Virtual Machines for AI Workloads
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This blog post covers the basics of reviewing virtual machine specs for AI workloads. Google Cloud Platform TPUs (Tensor Processing Units) are computer processors specifically designed to perform complex calculations at extremely high speeds and with minimum impact on system resource usage.
They’re ideal for training deep neural networks, especially ones that must process large amounts of data fast and repeatedly. However, this requires a fair amount of customization to meet individual workload needs. In particular, you need to be familiar with the different TPU generations so you know which options best suit your specific requirements.
GCP TPUs: What’s a TPU?
When Google first released the TPU product line in 2016, the hardware was based on the ARMv8 architecture. That same year, Google released a new architecture optimized for TPUs, called the Tensor Processing Unit v2.0. This new architecture made TPUs more efficient, more powerful, and more connected. The specific features of different TPU generations are listed in the table below.
TAU-40–40 core, 40 lane PCIe die with 40x2.5” SMs, 1MB L2, and 4GB GDDR5 TAU-60–60 core, 60 lane PCIe die with 60x2.5” SMs, 1MB L2, and 6GB GDDR5 TAU-80–80 core, 80 lane PCIe die with 80x2.5” SMs, 1MB L2, and 8GB GDDR5.
The TPUs Google uses are based on the ARMv8 architecture. The chips are clocked at 2 GHz, but they can reach up to 2.9 GHz on some configurations. The instruction set is the same as that used by the CPUs in Google’s main line of products, including the notebook chips used in Macs and Chromebooks.
GCP TPUs: What workloads are supported?
All Google Cloud Platform TPUs support the same workloads, which are as follows: Machine learning — Decentralized learning, including image recognition and natural language processing. AI — Predictive analysis, including image recognition, understanding speech, and language understanding.
How do Google Cloud Platform TPUs work?
Google Cloud TPUs are similar to the CPUs found in modern smart devices. They are highly customizable and have extensive functionality built-in. They measure performance and generate insight by looking at not only how much work is being done on the hardware but also the environment around it.
After the work is done, the TPUs complete the calculations and report the results to the host processor. For example, the AI workload mentioned previously might use the following steps to recognize images:
Recognize images, perform image search, discover new images, clean up images and store them in a cloud-based image gallery. After the work is done, the host computer can display the results or use them to train a model or make contextual recommendations.
Summary
Google Cloud TPUs are high-end, customizable outsourced AI accelerators. The work is done by the TPUs themselves, and the information is reported to the host processor. The TPUs are based on the ARMv8 architecture, which is similar to that found in modern smart devices. The TPUs are highly customizable and have extensive functionality built-in. TPUs are a great option for businesses building applications that need to handle large volumes of data or that require a higher level of customization.
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