Embedded VPU PC Computer
This post will provide you with everything you want to know about embedded VPU computers, as well as where you can buy them. Allow us to help you choose the best VPU computer to drive your deep learning and machine vision applications. VPU computing has taken off; check out the various embedded VPU PCs that Premio offers.
What is an Embedded VPU PC?
An embedded VPU computer is an industrial-grade PC that comes equipped with a VPU. VPUs are able to accelerate deep learning and machine vision algorithms because VPUs are specifically optimized for performing AI workloads. Furthermore, industrial VPU computers are designed and engineered with ruggedness in mind. That is, industrial VPU PCs are capable of withstanding deployment in challenging environments that are not friendly to regular PCs. Such systems are capable of withstanding exposure to dust, debris, shocks, vibrations, and extreme temperatures. If you’re looking to buy an embedded VPU computer, you should explore the wide variety of options that Premio has to offer.
So, for those of you who are curious, what is a VPU (vision processing unit)?
A VPU, short for vision processing unit, is a microprocessor that is specifically designed to accelerate machine vision algorithms. Machine vision algorithms are often used to analyze visual information, such as images and videos in search for patterns, identifying objects or persons, and searching for other information in videos or pictures. VPUs are also great for accelerating machine learning applications.
Typically, VPUs operate in conjunction with CPUs. The CPU offloads machine vision tasks to the VPU, freeing up the CPU for other tasks, such as running the OS and other applications.
Also, it’s worth noting that VPUs are different from GPUs (graphics processing units) in that they use significantly less power than GPUs when performing machine vision tasks. This is great for embedded VPU computers that are deployed at the edge in remote environments.
This is so because such systems must be power efficient, especially when they’re deployed in remote environments where stable power is not always available. In some circumstances, VPU computers deployed at the edge must be able to run on battery power until power is restored. If the VPU consumed too much power, the PC would not be able to run for long from battery power. As such, the power efficiency of VPUs when compared to GPUs allows the system to run on battery power when stable power is not available, therefore making them a great option for remote deployments where stable power is not always available.
How Are VPUs Integrated Into Embedded Computers?
VPUs are typically added to embedded PCs by plugging into a mini PCIe slot. For example, the Intel Movidius VPU Mini PCIe can be installed into an embedded system to accelerate machine vision applications for faster, more accurate video analytics, blazing-fast performance for high-quality image processing, computer vision, and deep neural networks.
(image credit: https://en.wikipedia.org/wiki/PCI_Express)
Furthermore, VPUs can be installed via USB ports. For example, the Intel Movidius Neural Compute Stick 2 is a VPU that can be easily and quickly installed by plugging into a USB Type-A port. The Intel Movidius Compute Stick is a great solution for organizations that want a compact embedded computing solution that can perform deep neural network inference analysis at the edge. The great thing about a low-powered embedded computing solution that utilizes the Neural Compute Stick is that it has a low power and thermal footprint, making it a great option for remote deployments where stable power is not always available.
VPU Acceleration vs. GPU Acceleration
Some organizations choose to configure their systems with hardware performance accelerators, such as GPUs (graphics processing units) or VPUs. Like VPUs, GPUs work as co-processors that are capable of accelerating deep learning (DL), artificial intelligence (AI), machine vision (MV), and high-end image processing workloads.
While some VPUs can be passively cooled via the use of heatsinks, almost all graphics cards require some form of active air-cooling using fans. This is so because graphics cards tend to consume a lot of power and therefore produce a ton of heat, requiring active air cooling. That said, GPUs are great for performance acceleration, making them ideal for autonomous vehicles, medical imaging, vision-based quality assurance, and other graphically intensive workloads.
When it comes to power, VPUs are much more power-efficient than GPUs because graphics processing units tend to be power-hungry.
When it comes to applications, VPUs tend to be more suited to perform object and image recognition, machine learning, and scalable image recognition. On the other hand, GPUs are more suitable for deep learning, artificial intelligence, and high-fidelity image rendering.
Overall, VPUs are more suitable for embedded applications because even though they are less powerful than GPUs, their small footprint and power efficiency makes them excellent for compact embedded computing at the edge.
That said, GPUs still have their place because their parallel architecture makes them ideal for deep learning inference analysis. For example, Nvidia’s GPUs have thousands of cores, enabling to perform computationally demanding tasks that CPUs are not capable of performing in a timely manner. However, the major setback is that GPUs are power-hungry, often consuming more than 200 watts of power.
So, how are GPUs integrated into industrial embedded computing solutions?
Industrial embedded computers are built on principles that include having a compact and fanless design. GPUs are different from embedded compact computing solutions in that GPUs are power-hungry, often consuming more than 200 watts of power, and so they require active cooling.
Premio has solved this problem by creating a two-compartment computer system. The first compartment houses the processor, chipset, storage, and I/O in a fanless design, while the second compartment only houses the GPU and provides active air cooling.
The main compartment is entirely fanless, while the GPU compartment is air-cooled, providing fans that can expel the GPU heat from the system. That said, GPU accelerated embedded PCs are still rugged, meaning they can still withstand deployment in challenging environments that experience frequent exposure to dust, debris, shocks, vibrations, and extreme temperatures.
You do sacrifice some shock and vibration resistance, but you gain a lot of performance for that small sacrifice thanks to the hardware acceleration that GPUs provide. Premio makes it very easy and convenient to swap out GPUs for other ones for service or in the event that a GPU fails. This allows for easy GPU replacement without having to replace the entire system.
Where Can You Buy a VPU Computer?
Premio has been designing and manufacturing embedded computers for over 30 years in the United States. We have a wide variety of embedded VPU computers that you can choose from. Options range from PCIe VPU PCs to embedded computing solutions with VPUs that can be installed externally onto system via USB Type-A ports.
At Premio, we have realized the benefits that VPUs bring to embedded computing. So we offer a number of embedded computers with VPUs, providing reliable and powerful computing solutions that are optimized for applications, such as deep learning inference, machine vision, and other vision applications. If you need help selecting an embedded VPU PC, do not hesitate to contact our embedded computing professionals. They will be more than happy to assist you with finding the VPU computing solution that meets your specific requirements.
Frequently Asked Questions (FAQs)
1. What is an industrial VPU computer?
A VPU computer is an embedded computer that utilizes a VPU (vision processing unit) to accelerate vision-based workloads, such as machine vision, image and object recognition, and high-resolution image processing. That said, you should not mistake a vision processing unit for a video processing unit. Vision processing units are used for AI acceleration, while video processing units are used for video processing.
2. What is a vision processing unit?
A vision processing unit, also known as a VPU, is a hardware accelerator for deep neural network inference, object recognition, facial recognition, and a variety of other vision applications. Computer systems equipped with VPUs offload vision tasks to the VPU (vision processing unit), which uses significantly less power than a GPU would to perform the same task.
3. What is the Intel Movidius?
The Intel Movidius is a VPU that accelerates demanding vision application and AI workloads. Intel Movidius has achieved this by coupling highly parallel programmable compute with workload-specific hardware acceleration. The Intel Movidius is energy efficient yet packs a punch when it comes to computing performance.
4. What is the Intel Myriad X?
The Intel Myriad X is the world’s first SoC (system-on-chip) that features a neural compute engine that is designed to accelerate deep learning and inference analysis at the edge. The Myriad X is specifically designed to run deep neural networks at very high speeds while consuming little power, enabling real-time analysis and decision making at the edge.