Want to upgrade your system but are confused between TPU and GPU? If yes, then this article is for you. Here in this article, I have explained everything you need to know about TPU and GPU. Here is TPU Vs. GPU, read the below article thoroughly to learn more.
The advancing technologies have made a considerable change in almost everything. Then how could artificial intelligence and machine learning be left behind? Various processors and accelerators are developing to support the complex applications emerging every day. CPU, GPU, and TPU are some examples of such processors.
With the changing demands of the user, changing workloads, the need for better technology, the CPU alone fails to shoulder the responsibility of these new requests. GPUs and TPUs are now the substitutes in the market for CPUs.
Now the question arises which one should you choose, GPU or TPU? If you are also struggling between the two, continue reading.
Table of Contents
What are the Major Differences Between CPU, GPU, and TPU?
Do you know the brains of the computer? Well, the processing unit designed for the general-purpose programming known as CPU functions as the computer’s brain. When computer graphics and AI workloads come into consideration, GPU is the one that makes it to the frame as an enhancer.
While the TensorFlow-based TPUs speed up machine learning workloads. They are one of Google’s custom-developed processors. Thus, the different processors specialize in dedicated functions effectively.
What is a GPU?
To accelerate the performance along with the use of CPU, a graphic processing unit(GPU) gets dedicated. The processor divides the complicated problems into many singled-out tasks via thousands of cores and works on them simultaneously.
Modern-day applications like bitcoin mining, graphic processing, video rendering, and even machine learning get performed owing to the domain of parallel computing.
The unmatched capabilities of accelerating the matrix operations and mixed-precision matrix calculations in a single operation justify the need for a GPU. Also, this rising essential component of the modern-day computer system is accountable for the worldwide AI boom.
Summary of the GPU
- Many cores are present.
- Effective parallel computing.
- Increased output.
- Perform several functions at once.
How do GPUs Work?
As mentioned earlier, the GPU holds expertise in parallel computing. It adds a high degree of value to the processor. Also, this specialization grants them the ability to perform so many tasks at once.
A CPU breaks the given number of tasks into simplified ones one by one. This functioning is the exact opposite when compared with the working of a graphic processing unit.
What is TPU?
Tensor processing units or TPUs are specified frameworks for deep learning or performing machine learning applications. This invention of Google came into their use in 2015, and then it got available for general use in the year 2018.
A normal processing unit might lack the required abilities to handle computational demands, perform accurate AI calculations, and maintain proper algorithms.
TPU is one of the domain-specific architectures of Google, it got designed to cater to the needs of the computer network workloads and less power consumption. The normal processing units often lag on speed, but with TPUs, one can relax. The struggle of memory access gets responsible for this, which gets eliminated in this framework.
Summary of TPU
- Very high output.
- Dedicated Matric Processor.
- Compatible with large neural networks.
- Much better than a CPU.
How do TPUs Work?
The memory’s parameter gets converted by the TPU into the matrix of multipliers or adders. Then from the converted memory, data gets loaded. The results extracted after executing the multiplications get passed onto the new multipliers.
Amidst everything, the process of adding things together also continues. The unique feature of this function is it requires no memory access for any calculations. If you wonder about the output, it is usually the synopsis of all the carried-out multiplications.
TPU Vs. GPU
Before choosing between the two, one needs to figure out how much one’s pocket allows. Both GPU and TPU bring a lot to the table regarding handling neural networks, deep learning, and even AI. But, TPU works faster than the former and also uses fewer resources.
So, if it falls within your budget, you can avail this framework. Even if the GPU is the one that gets chosen, it is also one of the good choices. Here is TPU Vs. GPU:
Frequently Asked Questions (FAQs)
These are some of our viewers’ most frequently asked questions about the best laptops under $500 for video editing. I hope that helped clarify some of your questions regarding the best laptop for video editing.
In case you have any other questions not answered here, feel free to ask them in the comments section below. We’ll do our best to answer them.
1. Is TPU faster than GPU?
Yes, TPU is faster than GPU. Also, it uses a lesser amount of resources and is capable of shouldering the responsibility of handling large neural networks also.
Both TPU and GPU are made for different needs; you should go with GPU if you work with graphics and play games, and you can go with TPU if you work more with AI and machine learning.
2. What is the difference between GPU and TPU?
A graphic processing unit(GPU) breaks down the number of tasks into many and then carries them out all at once. This performance enhancer aims at graphics and AI workloads.
On the other hand, Tensor processing units(TPU) are Google’s dedicated learning framework spanning over machine learning workloads.
3. Which is better for deep learning, GPU or TPU?
GPU and TPU are both made for different needs. If you want to step up the workflow of data sciences, GPU is the one you should choose.
But, if the purpose of your purchase is to speed up the learning process of a machine learning model, then you should buy TPU.
If you don’t keep up with the changing technologies, you might be left behind in the crowd. The modern time marks many alternatives or varieties of the same thing.
Similarly, different processors in the market offer their own set of advantages based on their specifications. Today, it finishes down to three options. One can expect more options to choose from as researchers will progress in the future.
We hope this article about TPU Vs. GPU is helpful to you. Do share which one you will purchase and why in the comment section below.