Introduction

Google has announced its latest Application-specific integrated circuit (ASIC), which will enable a platform for AI practitioners and provides faster internal algorithms of machine learning and the use of Google Cloud for their research. A Tensor Processing Unit or TPU is built for such individuals who want to achieve breakthroughs ranging from business, research, network security, medical diagnoses, and more.  Tensors are multi-dimensional arrays and matrices. In the cloud TPU, the parameters are loaded from memory into the matrix of multipliers and adders. Then, the data is loaded from memory. Then the results are passed to the next multipliers while taking summation after every multiplier operation. 

  1. What is Google Cloud TPU?
  2. How to use Google Cloud TPU
  3. Why is the Google TPU strategic?

1. What is Google Cloud TPU?

 A  custom-built machine learning ASIC or a cloud TPU powers Google products like Assistant, Gmail, Search, and Translate. Google’s second-generation Cloud TPUs are bringing faster computational performance to the TensorFlow community. Google Cloud TPU’s low cost and fast performance make it perfect for the teams who learn the machine programs for performing better. A user can also customize its own machine-learning solutions for real-life examples. It can be made simply by the user if he brings his own data, downloads a reference model, which is Google-optimized, and initiate the training. Cloud TPUs has the following four offerings:

  • Cloud TPU v2- It has 180 teraflops with 64 GB High Bandwidth Memory (HBM).
  • Cloud TPU v3- It has 420 teraflops with 128GB HBM.
  • Cloud TPU v2 Pod- It has 11.5 petaflops with 4 TB HBM and a 2-D toroidal mesh network.
  • Cloud TPU v3 Pod- It has 100 petaflops with 32 TB HBM and a 2-D toroidal mesh network.

Cloud TPU has the following features:-

  • A Model Library- consists of the cloud-TPU’s collection of improved models that provide accuracy, great performance, and quality in object detection, image classification, speech recognition, language modelling, and more.
  • Connecting the Cloud-TPUs with custom machine types is where the user can connect to cloud-TPUs from customized AI Platform Deep Learning VM Image types, which balances the memory, processor speeds, and the high-performing storage resources.
  • A Full Integration of the Google Cloud Platform-The data and analytical services of Google Cloud and Cloud TPUs are fully integrated with other Google Cloud Platform services. Thus, the user can get the benefit due to its networking, storage, and data analytics technologies.
  • Preemptible Clouds – Preemptible clouds are comparatively an economical option than on-demand examples; hence a lot of money can be saved. 

2. How to use Google Cloud TPU

To create a Google Cloud account and a Cloud-TPU Project, the following steps must be done:

  1. A google cloud account can be set up by creating a Cloud TPU Project. So, go to the Cloud Console and create a project.
  2. A billing account is to be made while setting up the project, and only after that, you will be charged for using the cloud resources.
  3. After the above steps, the storage, the Computer Engine VM, Cloud TPU resources need to be initialized.
  4. Every project is then allocated a default amount of quota for Cloud TPU resources. This quota is displayed on the Google Cloud Console.

Cloud TPU vs GPU:

TPU- It is an application-specific integrated circuit to accelerate AI calculations and algorithms. TPUs are custom build processing units to work for a specific app framework. Only Google manufactures them.

GPU- Graphical Processing Unit (GPU) is integrated into each CPU in some form. It helps to display what is going on in the CPU by rendering the graphical user interface visually. Some popular GPU manufacturers are – NVIDIA, AMD, Broadcom Limited.

3. Why is the Google TPU strategic?

Google TPU is a lot more strategic as compared to its cheaper alternative GPUs, and it can also deliver high performance if kept in the hands of talented and good designers. Also, Google made the technology available externally to enable the smooth functioning of the industry. This ASIC is probably going to be a success; hence more such ASICs are to be launched in the future. However, there can be challenges in launching ASICs, and such reasons can include limited functionality, expensive, complex designs, time-consuming, and more. Thus, ASICs like the TPU are only feasible economically if the costs can be recovered. 

The Google TensorFlow Framework and the TPU give data scientists and engineers a platform to support their product development and research. This gives them a competitive advantage as the Google team has control over both the hardware and the software for their Machine Learning enhanced products and services.

Conclusion

Through Google cloud’s TPU, Google is attempting to build a dominant position in the industry. The main focus is to provide good service at an economical rate; hence it is looking to expand its services ranging from optimized search services to Android capabilities to autonomous vehicles. For this purpose, the research is still going on, keeping in mind the long-term impact and the competitive advantage of this tpu cloud. The second-generation TPU is built with better research, and hence the upcoming generations are expected to be launched with better expertise.

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