A VPU is a unit that will be talked about more and more, even if it is now in the background. It is that this type of processor is used for a specific application, such as artificial vision used in a multitude of applications, from the industrial sector to autonomous vehicles, mobile devices, etc. Do you want to know what it is and its differences with a GPU?
What is VPU?
A VPU (Vision Processing Unit), or artificial vision processing unit, is a type of processor designed to accelerate the machine learning process and advance artificial intelligence technologies, particularly artificial vision or computer vision.
In short, it is an AI accelerator that can process various tasks such as image processing. This VPU is one of several specialized chips used in machine learning, similar to the GPU, but with some differences that we will see in detail later, since they are not the same, despite the similar applications that both have.
The VPN is used in convolutional neural networks, also like some GPUs, but it is dedicated. On the other hand, you also have to consider some issues when choosing a VPU, since they may also have greater or lesser capacity and performance. For example, some VPUs can process images with resolutions up to native 4K and support up to 8 directly connected sensors. Remember that the more sensors are supported and the better the resolution, the better the VPU and its accuracy will be.
Functioning
For a VPU to work properly, an encoder and decoder must be used, along with projects like the OpenVINO open-source toolkit.
On the other hand, preprocessing through APIs such as OpenCV or other existing libraries is also necessary. This way you can invoke models trained by TensorFlow, Caffe, etc.
The VPU, at the hardware level, is only responsible for accelerating these tasks using computing units that act as neural motors, units specially dedicated to accelerating loads related to artificial vision, etc.
VPU vs GPU: diferencias
As I mentioned above, the VPU and the GPU have similarities, but they are not the same. Here we are going to point out which are the most notable differences :
- The GPU is designed to be efficient at rasterization and texture mapping. Therefore, it has a different architecture than the VPU. The same goes for the VRAM memory, which is also designed to offer good performance in this type of task. However, as you know, the GPU can be used for other more generic tasks.
- The VPU is designed to optimize performance in computer or machine vision acceleration tasks. Although it may have similarities with the above, it is not exactly the same. In addition, it is usually designed to improve performance per watt, being very efficient.
- The GPU tends to have superior performance in general, the VPU is usually a more discrete device in this sense and intended for specific tasks.
- While the GPU market is usually the field of gaming, HPC and GPGPU, accelerated AI, etc., in the case of the VPU the target market is robotics, IoT, industry, virtual and augmented reality, smart cameras, autonomous vehicles, and other devices with integrated vision systems.
That is, a VPN is a type of artificial neural network accelerator and it would be found between a GPU, a DSP, a CPU, etc.
Applications and uses
Regarding the uses or applications that can be given to a VPU, we must highlight the following:
- To equip robots with computerized vision.
- For autonomous vehicles with vision and recognition sensors.
- In devices equipped with artificial vision of some kind, such as mobile devices.
- For the IoT that needs artificial vision.
- In the field of virtual reality, augmented reality, and mixed reality.
- In the industry for machinery that uses artificial vision to process the product, or inspection and quality control.
- Also for smart cameras and other smart or automated security systems.
- Any equipment that has to be endowed with object recognition or vision.
- Etc.
Advantages and disadvantages of its use
Among the advantages of using a VPU we have to highlight:
- Optimized Performance – Designed specifically for machine vision tasks, enabling them to process images and perform vision algorithms efficiently and quickly. And it is that its specialized architecture is optimized to speed up the processing of images and videos.
- Power efficient – They are very efficient in terms of power consumption, which makes them ideal for devices with limited resources, such as mobile devices or embedded systems. Its design allows for intensive computations with relatively low power consumption.
- Greater precision in vision tasks: Their specialized algorithms and features for machine vision allow them to achieve greater precision in the detection, classification, and recognition of objects in images and videos.
- Scalability – In some cases, they come as cards, in others, they can be external USB drives, so it’s easy to scale if more power is needed, adding more VPU devices when required.
On the other hand, we also have some disadvantages, such as:
- Limitations on general-purpose tasks: VPUs are designed for specific machine vision tasks and may have limitations on handling general purpose tasks that are not related to vision. This means that they are not as versatile as GPUs.
- Dependency on software and support: they require specialized software and libraries to make the most of their potential. This implies that an adequate development ecosystem and active support from the manufacturer are needed to guarantee good operation and correct integration.
Practical examples
You should know some commercial examples that we have with VPU, we must highlight some well-known cases such as:
- Intel Movidius Myriad X and 2 – These are devices designed and manufactured by Intel for these types of tasks. They include a Myriad VPU and achieve quite good performance. In fact, it can be found in applications like Google Clips, Google Project Tango, and some DJI drones, among others.
- Google Pixel Visual Core or PVC – It is another device that is fully reprogrammable and acts as an AI accelerator for Google Pixel mobile devices.
- Microsoft HoloLens – This Microsoft product also contains an integrated VPU to speed up certain tasks. In this way, it can be improved in augmented reality.
- MIT Eyeriss – Another known design with convulsive neural networks.
- Yann LeCun NeuFlow: it is a VPU design implemented in an FPGA and similar in characteristics to the previous one, but using a Dataflow architecture.
- Mobileye EyeQ – is another design created by the Mobileye company for mobile devices.
- NVIDIA PVA or Programmable Vision Accelerator – It is basically a 7-way VLIW architecture vision processor for high-performance applications.
There are also other similar products, such as IBM TrueNorth, Qualcomm Zeroth Neural, etc.
Don’t forget to comment with your doubts…