Deep learning has made several leaps in recent years, from beating the world chess champion to autonomous driving. Such achievements require a sophisticated technique, which in turn demands a complex system. As the need for deep learning increases, so does the demand for better and more complex hardware equipment.
The evolution of intelligent applications has been accelerated by artificial intelligence and machine learning technology. Semiconductor manufacturers are continually creating processors and accelerators to cope with more sophisticated applications. Initially, computers simply had a CPU, then came the GPU, and finally TPU.
The demand for speedier hardware is continuously growing as the IT sector evolves and discovers new ways to use computers.
The Central Processing Unit (CPU), Graphics Processing Unit (GPU), and Tensor Processing Unit (TPU) are processors with specific purposes and architectures. These vary from memory subsystem architecture, compute primitive, performance, purpose, usage, and manufacturers. Developers working on computer systems should be aware of these differences.
The distinction between the CPU, GPU, and TPU is that the CPU is a general-purpose processor that handles all of a computer’s logic, computations, and input and output. On the other hand, GPU is an additional processor used to improve the graphical interface and perform high-end tasks.
TPUs are strong custom-built processors that are used to execute projects developed using a specific framework called TensorFlow. When it comes to memory-intensive operations, GPUs and TPUs architectures are used. Following, each of the processors is explained in a detailed manner.
CPU stands for Central Processing Unit. CPU is also regarded as the brain of a computer system. It is the computer’s fundamental hardware that executes computer program instructions. The CPU is also the arithmetic logical unit and control unit of a computer system. It controls all the input, processing, and output operations in a computer. The CPU also operates operating systems. For a general-purpose programming problem, the cache and the memory are designed optimally.
However, CPUs cannot perform well for operations such as machine learning, deep learning model inference, graphics rendering, etc. There are optimized Pytorch CPU models present, especially for machine learning. However, for huge datasets with memory-intensive operations, advanced processors such as GPUs and TPUs are preferred.
GPU stands for Graphical Processing Units. A GPU (graphics processing unit) is a specialized processor that synchronizes with the CPU to boost performance. Simply, GPUs help in rendering the information being processed through a CPU. A GPU, in comparison, has thousands of cores that can break down complex issues into thousands or millions of individual jobs and solve them simultaneously.
Thousands of GPU cores are used in parallel computing to improve a variety of tasks, including graphics processing, video rendering, machine learning, and even cryptocurrency mining. The main concern with GPUs is cost. Popular GPU services like AWS, AZURE, GCP, etc., are costly for normal operations. But, there are also some free platforms like Colab, Kaggle where free GPU experimentations can be done (with some limitations).
The tensor processing unit (TPU) is a type of application-specific integrated circuit (ASIC) used to accelerate AI calculations and algorithms. Google created TPU from scratch for the Tensorflow Package; they began using it in 2015 and made it public in 2018. TPUs are mainly used for neural network machine learning.
TPUs are processing units that have been specially built to function with Tensorflow Package. An open-source machine learning platform with cutting-edge tools, libraries, and community allows users to design and deploy machine learning applications quickly. Cloud TPU enables you to use TF to execute your machine learning experiments on TPU. Google’s TPU is a powerful and flexible processor that allows researchers and developers to execute models using high-level TensorFlow APIs.
Besides the cost and its main limitation, it is only used for TensorFlow projects by researchers and developers. Also, it has a difficult learning curve compared to straightforward processors like CPU and GPU.
A CPU should be able to handle tens of operations per cycle, while GPU can handle tens of thousands of operations per cycle. More than that, the TPU can handle up to 128,000 operations per cycle. TPU has maximum throughput and efficiency. Models that required weeks to train through a GPU or other hardware can now take mere hours to train on TPU.
Initially, computers had only CPU, then GPU came into light, and now finally we have TPU. The tech industry is growing at a rapid rate with the need for faster and faster computing devices. CPUs, GPUs, and TPUs are distinctive in their own way, fulfilling the need for faster computations.