TPU Vs GPU: Differences, Pros, Cons Which is Fast (Guide)

I’m going to talk about a really important question, one that I think a lot of people want to know the answer to. What are the main differences between TPU and GPU? Let’s get started! 

Tpu Vs GPU

What is a TPU and GPU?

TPU

The Tensor Processing Unit or TPU is a specialized processing unit by Google that was built for two purposes. First, Google used it to help improve their own products such as image search, translation, AlphaGo and other services by providing better machine learning results. Second, they released this powerful hardware into the cloud so other people can use it to get better results in their machine learning tasks.

GPU

The GPU or Graphics Processing Unit is a specialized hardware that was originally designed to render graphics for games or other programs on your screen, but now it’s used across many different fields outside of graphics as well. One example would be machine learning where GPUs are used to accelerate the training of different models. For example, if you train an image classification model by multiplying two 32×32 matrices together it would take way too long even on a fast CPU. However, if you can accelerate these matrix multiplications with your GPU then training this same model might take hours instead of days or weeks.

Main  differences between tpu vs gpu

Both TPU and GPUs are programmable processors with thousands of cores, that are used in most modern computers to run different applications. But the main differences between them are their purpose, programming language, source of power and performance. Let’s take a closer look at each one of these differences.

expensive hardware

First of all, the main difference is that the TPU (Tensor Processing Unit) is an ASIC (application-specific integrated circuit), while GPU is a general purpose processor. What this all means in simple terms for us users is that there are no GPUs implemented with tensor cores and we can only work on GPUs which don’t have tensor processing units. The second main difference is that CPU/GPU are widely available, while TPU can only be found inside Google’s data center.

performance comparison

I think an other big difference is that GPUs are not hand-crafted for deep learning tasks like the TPUs are. This should mean that the TPUs are better suited for deep learning tasks than GPUs, even though there is no public benchmark data that proves this. TPUs are designed to minimize the amount of memory used which allows them to achieve higher throughput at lower power consumption compared to other chips.

High performance requires high precision

The third difference is that TPUs are designed to achieve high performance with low precision while GPUs are designed to achieve both high performance and high precision. This means that TPUs can achieve a higher throughput by reducing the number of bits they use for computations.

High Precision vs High Performance

The fourth difference is that GPUs allow more flexibility in changing computation precision on demand.  To get a better understanding of how GPUs and CPUs work on a more technical level, you can take a look at this article.

GPU vs TPU – Tensorflow benchmark

Last but not least there was a benchmark released by Google on which they compared an unoptimized version of the MNIST convolutional network using various frameworks on both GPUs and CPUs. You can find more details about that here . The results show that the TPU outperforms the K80 GPU (Tesla K80) by 27% while using 1/10 of the power.

TPUs outperform GPUs

To summarize, there are a number of differences between a TPU and a GPU which makes the TPU better suited for deep learning tasks than regular CPUs. This, however, doesn’t mean that GPUs cannot be used for these tasks. Both devices can achieve high accuracy and good throughput with low power consumption. I think it is clear now that any comparison between TPU and GPU has to be done on a use case basis as they both have their advantages and disadvantages.

Overall, I think it’s safe to say that TPUs are designed with deep learning performance in mind while GPUs are simply good for pretty much anything else.

GPU vs TPU – Use Case

In the end, I think Google’s benchmark shows that TPUs outperform GPUs when using a low precision model. This makes it a good fit for inference tasks where a high throughput is needed and a lower accuracy can be accepted. When you need a higher level of precision, GPUs should be your choice as they allow for easier changes in computation precision.

Possible applications for TPUs are image/audio/video processing, speech recognition, natural language processing (NLP), search, optimizer of large-scale systems (e.g., databases or server farms), deep learning inference on personal devices, parallel processing in general and many more . So now you know about the differences between TPUs and GPUs. I hope this short article was helpful.

TPU vs GPU – PROGRAMMING LANGUAGE

The second difference between TPU and GPU is their programming language. As I mentioned before, the TPU can only run machine learning tasks, so it’s programmed in C++ to execute commands on thousands of cores at once. GPUs are programmed in either CUDA or OpenCL , depending on the manufacturer, but they are designed to use general-purpose programming languages, so there is a speed disadvantage when running machine learning tasks.

TPU vs GPU – SOURCE OF POWER

The third main difference between TPU and GPU is their source of power. The Tesla P40 from NVIDIA draws around 250Watts, while the TPU v2 draws around 15 Watts. This means that the NVIDIA Tesla P40 uses 25x more power than the TPU v2 to run a machine learning task.

Pros and cons of tpu and gpu

TPU

+ Very fast at matrix multiplications, convolutions and other tasks required for machine learning

+ Very reliable due to the lack of moving parts (no fan)

– Expensive hardware that is currently only available in the cloud. However, there are plans to sell them to businesses later this year as well as releasing a version that will fit in your PC.

– Limited to only running Google’s TensorFlow framework at the moment, which isn’t a problem for most people but might be a deal breaker for some.

GPU

+ Can be installed in your own computer or server and can run any machine learning program you want. This means that research into new models and algorithms will be easier since you can run them much faster.

+ More accessible for people who don’t have a lot of money, basic GPU hardware is quite cheap and easy to add into your own computer or server.

– Currently very slow when it comes to matrix multiplications, convolutions and other tasks required for machine learning. This means it will take a very long time to train even relatively simple models, but it can be solved by adding more GPUs to your computer.

– Less reliable due to the fact that they have fans and are more exposed to temperature changes. This means you need proper cooling in your datacenter or office if you want this hardware to last for many years.

How does TPU work?

The TPU is a symmetric multiprocessor, which means that it’s a computer with many CPUs. In fact, the TPU has 64 cores and each core runs at 700 MHz. Each of these cores can run one operation at a time or it can do different operations in parallel. It does this by using something called warp-shuffle so it can execute many operations in parallel. But what is warp-shuffle? The warp-shuffle unit distributes the 16 32-bit numbers from each thread into 8 bundles of 4 numbers each before sending them to other threads for processing. These are called warps because they consist of a maximum of 16 threads and also because they’re 32-bit in size. If they were to be sent individually then each thread would have to wait for the previous threads before continuing, which is why they are bundled together into these warps of 16 numbers and sent off so multiple bundles can be processed at once. But how does the warp-shuffle unit determine where 32-bit numbers go exactly? This is done by first determining the 8-bit boundaries between integers and then mapping those integers to threads. If you want some more details on how this works, check out section 5 of the paper on TPUs .

What makes the TPU so fast?

The TPU only takes around 70 cycles to perform a multiply and accumulate, which is simply an operation that multiplies two 8-bit integers together and adds the result to another 8-bit integer. The TPU can also perform 256 8-bit operations at once thanks to warp shuffle, so it’s capable of performing 2,048 32-bit operations per cycle. To compare this with other hardware, today’s GPUs are only capable of performing about half as many operations per cycle. However, GPUs tend to be faster because they have more cores and they can run at higher clock speeds. But the TPU is still faster than CPUs for this type of work so it’s not really comparable. It’s also important to remember that you don’t need a high-end TPU to get these results. GPUs can use faster clock speeds, more cores and they come in a smaller package than the TPU but CPUs do not have this type of specialization so there is no way they could keep up with the TPU’s capabilities today.

What makes the GPU so fast?

GPUs are really good at multiplying matrices together. In fact, you can scale a GPU pretty much infinitely if you use fast enough hardware because it’s so good at multiplying matrices. GPUs are also really good at taking advantage of the warp-shuffle unit, which is why they tend to be faster than CPUs even though many other types of calculations take longer on them. However, this does come at a cost. GPUs are also very power hungry and they need a lot of cooling, which is why you don’t want to have one in your home computer.

What makes the TPU so energy efficient

GPUs can scale infinitely because their configuration is flexible. In fact, some consumer hardware comes with up to 4 GPUs on a single card. CPUs, on the other hand, are limited to about 8 cores and they access memory in a linear fashion. This means that if you want more performance you need a bigger chip with more cores, which also uses a lot of power and produces a lot of heat. The TPU is limited to 64 cores, but it can do 256 8-bit operations per clock cycle. Because the TPU is not flexible it doesn’t need to use memory in a linear fashion and can instead use multiple levels of cache effectively, which makes it much more energy efficient than other types of hardware that are designed for flexibility.

Conclusion

TPU outperforms GPUs at training time, and they both perform really fast for inference tasks. We do not know exactly where the limit of parallelization is, but we believe that these chips can be made faster with more work. The TPUv2 has already been shown to be 3x faster than the TPUv1.