TPU vs. GPU: Comparative differences in performance and speed in actual scenarios

In this article, we will compare TPU vs GPU. But before we dive in, here’s what you need to know.

Machine learning and artificial intelligence technologies accelerate the development of intelligent applications. To this end, semiconductor companies continue to create accelerators and processors, including TPUs and CPUs, to handle more complex applications.

Some users are having trouble understanding when a TPU is recommended and when a GPU is used to complete their computer tasks.

The GPU, also known as the Graphics Processing Unit, is your PC’s video card that provides you with a visual and immersive PC experience. For example, if your PC does not detect the GPU, you can follow simple steps.

To better understand these situations, we also need to clarify what a TPU is and how it compares to a GPU.

What is TPU?

A TPU or Tensor Processing Unit is an Application Specific Integrated Circuit (IC), also known as an ASIC (Application Specific Integrated Circuit), used for a specific application. Google created TPU from scratch, began using it in 2015, and made it available to the public in 2018.

#TPU is available as a minor silicon or cloud version. To accelerate machine learning of neural networks using TensorFlow software, cloud TPUs solve complex matrix and vector operations at blazing speeds.

With TensorFlow, the Google Brain team has developed an open source machine learning platform that allows researchers, developers, and enterprises to build and operate AI models using Cloud TPU hardware.

When training complex and robust neural network models, TPU reduces the time to reach accurate values. This means that training a deep learning model that might take weeks takes a fraction of that time using GPUs.

Are TPU and GPU the same?

They are highly different in architecture. The graphics processing unit is a processor in its own right, although it is piped into vectorized numerical programming. GPUs are actually the next generation of Cray supercomputers.

The TPU is a coprocessor that does not execute instructions itself; the code is executed on the CPU, which provides a flow of small operations to the TPU.

When should I use TPU?

TPUs in the cloud are tailored for specific applications. In some cases, you may prefer to use a GPU or CPU to perform machine learning tasks. In general, the following principles can help you evaluate whether a TPU is the best choice for your workload:

• Matrix calculations dominate the model
• In the model's main training loop , there are no custom TensorFlow operations
• They are models trained over weeks or months
• They are large models with a wide range of effective batch sizes.

Now let’s get straight to the TPU vs. GPU comparison.

What is the difference between GPU and TPU?

TPU vs. GPU Architecture

The TPU is not a highly complex piece of hardware and feels like a signal processing engine for radar applications rather than a traditional X86 derived architecture.

Although there are many matrix multiplications and divisions, it is more like a coprocessor than a GPU; it only executes commands received by the host.

Because there are so many weights to be input to the matrix multiplication component, the TPU's DRAM runs in parallel as a single unit.

In addition, since the TPU can only perform matrix operations, the TPU board is connected to the CPU-based host system to complete tasks that the TPU cannot handle.

The host is responsible for transferring data to the TPU, preprocessing, and retrieving details from cloud storage.

#The GPU is more concerned with having available cores for applications to work on than accessing a low-latency cache.

Many PCs (clusters of processors) with multiple SMs (Streaming Multiprocessors) become single GPU gadgets, each containing a first-level instruction cache layer and accompanying cores.

An SM typically uses two cached shared layers and one cached private layer before fetching data from global GDDR-5 memory. GPU architecture can tolerate memory latency.

The GPU operates with a minimum number of memory cache levels. However, since the GPU has more transistors dedicated to processing, it is less concerned with the time it takes to access data in memory.

Because the GPU is always occupied by enough computation, possible memory access delays are hidden.

TPU vs. GPU Speed

This original TPU generates targeted inference using a learned model rather than a trained model.

TPUs are 15 to 30 times faster than current GPUs and CPUs on commercial AI applications using neural network inference.

In addition, TPU is very energy-efficient, with TOPS/Watt values ​​increased by 30 to 80 times.

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So when doing a TPU vs. GPU speed comparison, the odds are stacked in favor of the Tensor Processing Unit.

TPU vs. GPU Performance

The TPU is a tensor processing machine designed to accelerate Tensorflow graph computations.

On a single board, each TPU delivers up to 64 GB of high-bandwidth memory and 180 teraflops of floating-point performance.

The comparison between Nvidia GPU and TPU is shown below. The Y-axis represents the number of photos per second, while the X-axis represents the various models.

TPU vs. GPU Machine Learning

The following are the training times for CPU and GPU using different batch sizes and each Epoch iteration:

• Number of iterations/epochs: 100, batch size: 1000, total epochs: 25, parameters: 1.84 M, model type: Keras Mobilenet V1 (alpha 0.75).

Accelerator	GPU (NVIDIA K80)	Thermoplastic Polyurethane
Training accuracy (%)	96.5	94.1
Validation accuracy (%)	65.1	68.6
Time per iteration (milliseconds)	69	173
Time per epoch (s)	69	173
Total time (minutes)	30	72

##Iterations/epoch: 1000, Batch size: 100, Total epochs: 25, Parameters: 1.84 M, and Model type: Keras Mobilenet V1 (alpha 0.75)

AcceleratorGPU (NVIDIA K80)Thermoplastic PolyurethaneTraining accuracy (%)97.496.9Validation accuracy (%)45.245.3Time per iteration (milliseconds) 185252Time per epoch (s)1825Total time (minutes)1621

As can be seen from the training time, using a smaller batch size, TPU requires longer training time. However, as the batch size increases, TPU performance gets closer to GPU.

So when doing a TPU vs GPU training comparison, a lot has to do with epochs and batch size.

TPU vs. GPU Benchmarks

With 0.5 Watts/TOPS, a single Edge TPU can perform 4 trillion operations per second. There are several variables that affect how well this translates into application performance.

Neural network models have different requirements and the overall output depends on the USB accelerator device's host USB speed, CPU, and other system resources.

With this in mind, the graph below compares the time taken to perform a single inference on the Edge TPU using various standard models. Of course, for comparison purposes, all models run are TensorFlow Lite versions.

Please note that the given data above shows the time required to run the model. However, it does not include the time required to process input data, which varies by application and system.

Compare the results of GPU benchmarks to user-expected game quality settings and resolutions.

Based on the evaluation of over 70,000 benchmarks, we carefully constructed sophisticated algorithms to generate 90% reliable estimates of gaming performance.

While graphics card performance varies from game to game, the comparison chart below gives a broad rating index for some graphics cards.

TPU and GPU prices

They have a big price difference. TPUs cost five times more than GPUs. Here are some examples:

• Nvidia Tesla P100 GPU $1.46 per hour
• Google TPU v3 $8.00 per hour
• TPUv2 with GCP on-demand access per hour $4.50

If cost optimization is your goal, you should choose a TPU only if it can train a model 5 times faster than a GPU.

What is the difference between CPU, GPU and TPU?

The difference between TPU, GPU and CPU is that CPU is a non-specific purpose processor that handles all calculations, logic, input and output of the computer.

A GPU, on the other hand, is an additional processor used to improve the graphical interface (GI) and perform high-end activities. TPUs are powerful purpose-built processors used to execute projects developed using specific frameworks such as TensorFlow.

We categorize them as follows:

• Central Processing Unit (CPU) – Controls all aspects of the computer
• Graphics Processing Unit (GPU) – Improves the computer’s graphics Performance
• Tensor Processing Unit (TPU) – ASIC designed specifically for TensorFlow projects

Does NVIDIA make the TPU?

Many have wondered how NVIDIA will respond to Google's TPUs, but we now have the answer.

Rather than worry, NVIDIA has successfully repositioned the TPU as a tool that can be used when it makes sense, but still keeps its CUDA software and GPUs at the forefront.

It maintains a control point for IoT machine learning adoption by making the technology open source. The danger with this approach, however, is that it could inform a concept that could challenge NVIDIA's long-term goals for data center inference engines.

Is GPU or TPU better?

In conclusion, we must say that although there are additional costs involved in developing algorithms that can efficiently use TPUs, the reduced training costs often outweigh the additional programming expenses.

Other reasons to choose a TPU include the fact that v3-128 has 8 G of video memory over Nvidia GPUs, making v3-8 a better choice for processing large data sets related to NLU and NLP.

Higher velocity may also lead to faster iterations in the development cycle, leading to faster and more frequent innovation, increasing the likelihood of market success.

TPUs outperform GPUs in speed of innovation, ease of use, and affordability; consumers and cloud architects should consider TPUs in their ML and AI plans.

Google's TPU has enough processing power that users must coordinate data input to ensure it is not overloaded.

With it, total comparison of TPU vs GPU. We'd love to know your thoughts, see if you've done any testing, and what results you received on TPU and GPU.

Remember, you can enjoy an immersive PC experience with any of the best graphics cards for Windows 11.

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