Introduction
Rugged Industrial Computers in A Nutshell
In the Industry 4.0 scene, where seamless functionality is essential, rugged industrial computers emerge as the perfect solution for performance and reliability. These computers are built tough, capable of handling wide temperature ranges, shrugging off shocks and vibrations, and some even come with a rugged fanless design. From bustling factory floors to cutting-edge IoT setups, these computers are the go-to solution for reliability.
How Can GPUs Power Up Rugged Edge Systems?
GPUs are no longer just limited to fancy graphics and gaming. With the advent of machine learning, AI, deep learning, and a vast range of other applications, GPUs have become a crucial component in rugged computing systems. Their robust design, coupled with efficient thermal management, ensures the reliability and longevity of rugged edge computing solutions while also highly boosting computing performance, making them an essential part of various industrial applications.
Rugged Industrial Computers and Their Challenges
What Are Rugged Industrial Computers?
A rugged industrial computer is a computing system designed for extreme conditions. Built to endure harsh environments, these compact and durable computers operate reliably in challenging settings such as defense, medical lab, and industrial automation applications. They resist extreme temperatures, shocks and vibrations, and are often customized to meet specific needs. With a focus on reliability and longevity, rugged industrial computers play a critical role in scenarios where standard computers would falter.
What Are the Challenges Rugged Industrial Computers Face?
Rugged industrial computers confront a unique set of challenges, primarily stemming from the environments they operate in and the critical nature of their applications.
Harsh Environments
Rugged industrial computers often find themselves deployed in environments characterized by extreme temperatures, humidity, dust, and vibrations. From scorching deserts to freezing tundra, these systems need to function reliably despite the adversities of the surroundings.
Reliability Demands
In critical applications such as defense, aerospace, and industrial automation, reliability is non-negotiable. Rugged industrial computers must deliver consistent and dependable performance, minimizing the risk of failures that could have severe consequences in mission-critical scenarios.
Performance Demands
From real-time data processing in the rugged edge to complex calculations in surveillance applications, these systems must deliver robust performance under demanding workloads.
Integration Challenges
Rugged industrial computers often need to interface with diverse sensors and peripherals. Ensuring seamless integration and compatibility with various components can be challenging.
Thermal Management
Efficiently managing heat generated during high-performance computing is vital. Rugged edge computers need effective thermal solutions to prevent overheating, especially in compact form factors and challenging climates.
Power Efficiency
Operating in remote or off-grid locations, rugged computer systems often face power constraints. Striking a balance between powerful computing and low power consumption is crucial for sustained operation.
As we explore the world of rugged industrial computers and the challenges they are facing, we'll shed light on the innovative solutions that make these systems indispensable in critical industries.
How NVIDIA GPUs Set Your Products Apart?
NVIDIA GPU is one of these innovative solutions. Equipped with high-end technologies like tensor cores and CUDA cores, NVIDIA GPU can accelerate AI computing at industrial scale while enhancing the ruggedness of industrial computers with its thermal management and power efficiency features.
The NVIDIA GPUs: A Perfect Fit
What Are NVIDIA GPUs?
NVIDIA GPUs (Graphics Processing Units) are specialized processors designed for rendering graphics and performing parallel computations. While traditionally known for their role in powering graphics for gaming and multimedia, NVIDIA GPUs have evolved into powerful accelerators for a wide range of tasks, including scientific simulations, artificial intelligence (AI), machine learning (ML), and complex data processing.
NVIDIA's GPU architecture, CUDA (Compute Unified Device Architecture), enables developers to harness the immense parallel processing power of GPUs for general-purpose computing tasks, extending their utility beyond graphics rendering.
What Is the Difference Between GPU and CPU?
The main difference between CPUs and GPUs comes from the number of cores. In a modern CPU, you'll find multiple complex, high-performance CPU cores. Four, six, and eight cores are typical for mainstream computers. Typically, CPU has a few powerful cores optimized for sequential processing. While in GPU, there are a larger number of smaller, specialized cores optimized for parallel processing, which allows for handling repetitive tasks simultaneously.
NVIDIA’s GPU family
Before we delve into NVIDIA’s GPU family, here are two basic types of GPU we need to focus on: discrete GPUs and integrated GPUs. A Discrete GPU is an external graphics processor that is apart from the central processing unit. GPU has its own dedicated memory that is separated from the CPU. Discrete GPUs are typically attached to the PCIe x16 slot on the motherboard. In contrast, integrated GPUs are embedded alongside the CPU on the SoC integrated circuit.
Among the wide array of NVIDIA GPUs, we simply break it down into gaming and consumer use, professional and embedded systems use GPUs. NVIDIA’s GeForce, and NVIDIA Ampere Architecture GPUs each have a different focus. GeForce GPUs are mostly focused on general use, gaming, or machine learning purposes, while NVIDIA Ampere Architecture GPUs are tailored for enterprise workstations, 3D graphic designs, and embedded systems.
Enterprise and Embedded Use GPUs
NVIDIA Ampere RTX GPU
The NVIDIA Ampere architecture builds on the power of RTX to significantly enhance the performance of rendering, graphics, AI, and compute workloads. These RTX GPUs are designed for professional users who require intense graphic processing demands, such as those in workstations, embedded systems, and engineering simulations. Enterprises that run computer-aided design (CAD), computer-aided engineering (CAE), computer-generated imagery (CGO), digital content creation (DCC), scientific calculations, and machine learning applications can benefit from NVIDIA RTX GPUs, which offer higher memory bandwidth, larger memory size, higher TDP, faster clock speed, and greater stability than GeForce RTX GPUs.
Gaming and Consumer Use GPU | NVIDIA GeForce
GeForce-GTX
NVIDIA GeForce GTX is the GeForce 16 series that initially was introduced back in 2008. GTX stands for Giga Texel Shader eXtreme. The microchip design of the GTX series is based on Turing architectures and it uses Cuda cores that can perform multiple matrix calculations in parallel at a high speed. Initially, the GTX series gained popularity for processing graphics-intensive applications such as games and graphics engines. However, with the advancement of technology, machine learning models are now utilizing thousands of Cuda cores to run their CNN (convolutional neural network) algorithm.
GeForce-RTX
RTX stands for Ray Tracing Texel eXtreme, is a higher-end version of GTX with more additional features added. RTX also uses Turing microarchitecture with thousands of CUDA cores. Further, they are equipped with Tensor cores for machine learning applications that are much faster and more efficient than the CUDA cores. In short, instead of executing multiprocessing calculations, Tensor cores can perform multiple multiprocessing calculations simultaneously at once. RTX GPUs can execute real-time ray tracing for complex graphics. Ray tracing basically calculates all the light angles in video games while in action, which will show a much more realistic reflection, refraction, textures, and materials of its surroundings in real time.
How Can NVIDIA GPUs Benefit Industrial and Embedded Computers?
Graphics and Visualization Enhancement
NVIDIA GPUs are renowned for their graphics processing capabilities thanks to technologies like ray tracing, tensor cores, etc. In industrial computers, especially those involved in CAD (Computer-Aided Design), simulations, and 3D visualization, robust graphics capabilities are essential for accurate representation and analysis of complex data.
Here are some of the key technologies that can help with the graphics and visualization enhancement:
Ray Tracing
Ray tracing is a rendering technique that simulates the way light interacts with objects, producing highly realistic lighting, shadows, and reflections.
It can enhance visual fidelity by providing more accurate lighting effects, reflections, and shadows, resulting in a more lifelike and immersive visual experience.
Tensor Cores
Tensor Cores are specialized processing units designed for accelerating matrix operations, primarily used in machine learning and AI workloads.
In graphics, Tensor Cores can be used for AI-driven features such as super-sampling and denoising, as a result, improved image quality and smoother visual effects.
DLSS (Deep Learning Super Sampling)
DLSS is an AI-driven technology that uses machine learning to upscale lower-resolution images in real time, providing higher-quality visuals without a significant performance hit.
DLSS improves graphics performance by rendering at a lower resolution and then using AI to upscale, which helps to deliver visually appealing images with reduced computational load.
Parallel Processing for Improved Performance
Parallel processing is a computing paradigm where multiple processes or tasks are executed simultaneously, dividing a complex problem into smaller, more manageable parts that can be solved concurrently.
Industrial applications often involve heavy computational workloads. NVIDIA GPUs, with their parallel processing capabilities, excel in handling complex tasks simultaneously, providing a significant boost in overall performance.
Power Efficiency and Thermal Management
Power Efficiency and Thermal Management in GPUs is a crucial aspect, especially considering the high-power consumption of modern graphics cards. Power efficiency is essential not only to optimize performance but also to manage heat generation and adhere to power constraints, particularly in scenarios with limited power availability.
Here Are Key Aspects of Power Management in GPUs:
Dynamic Voltage and Frequency Scaling (DVFS)
- Dynamic adjustments: GPUs can dynamically scale their voltage and frequency based on the workload. When high performance is required, the GPU operates at higher frequencies and voltages, and vice versa during periods of lower demand. This dynamic adjustment, known as DVFS, helps balance performance and power consumption.
Power Gating
- Selective power shutdown: Power gating involves selectively shutting down power to specific parts of the GPU that are not actively in use. This helps reduce power consumption during idle or low-demand periods, contributing to overall power efficiency.
Clock Gating
- Clock adjustments: Clock gating involves adjusting the clock frequency of different GPU components. By lowering the clock frequency during periods of lower demand, the GPU reduces power consumption without sacrificing performance when it's not necessary.
Power States
- Different power states: GPUs can operate in various power states, including active, idle, and sleep states. Transitioning between these states based on workload and user activity helps optimize power consumption.
Key Components of Thermal Management in GPUs Include:
Cooling Solutions
- Fans: Many GPUs are equipped with cooling fans to dissipate heat. These fans help move air across heatsinks or heat pipes to carry away heat generated by the GPU.
- Heatsinks: Heatsinks are metal structures designed to absorb and disperse heat. They are often in direct contact with the GPU, providing a surface for heat transfer.
Heat Pipes
Heat pipes are sealed tubes containing a liquid that vaporizes at the GPU's hot end and condenses at the cooler end. This phase change helps transfer heat away from the GPU efficiently.Thermal Interface Materials (TIM)
Thermal paste or pads: TIM is applied between the GPU and the heatsink to improve thermal conductivity. It ensures better heat transfer from the GPU to the cooling components.
Thermal Sensors
GPUs incorporate thermal sensors to monitor the temperature of various components. These sensors provide real-time data to the GPU's thermal management system, allowing it to adjust fan speeds and clock frequencies dynamically.Overheat Protection
Thermal throttling: To prevent overheating, GPUs often have built-in thermal protection mechanisms. If the temperature surpasses a predefined threshold, the GPU may reduce its clock speed or take other measures to lower heat generation and protect itself from damage.
Fan Curve Control
The fan speed is often controlled based on a predefined fan curve or profile. The fan speed increases as the GPU temperature rises, optimizing cooling performance.
Accelerating AI and Machine Learning
Because of unique features like parallel processing, CUDA, and Tensor Cores, NVIDIA GPUs can accelerate AI and Machine Learning.
Parallel Processing Architecture
GPUs are designed with a large number of cores optimized for parallel processing. This architecture allows GPUs to handle many calculations simultaneously, making them exceptionally suited for the parallelizable nature of AI and ML algorithms.Matrix Operations and Linear Algebra
AI and ML algorithms often involve heavy matrix operations. GPUs are highly efficient in performing these matrix multiplication tasks, a key operation in neural network computations.Tensor Cores
NVIDIA GPUs feature Tensor Cores are specifically designed for tensor operations common in deep learning tasks, which accelerate the processing of large tensors used in neural networks.Deep Learning Frameworks
Major deep learning frameworks, such as TensorFlow and PyTorch, have been optimized to leverage GPU capabilities. These frameworks offload computations to the GPU, taking advantage of its parallel processing power.Model Training and Inference
- Training Neural Networks: GPU acceleration significantly speeds up the training phase of neural networks, allowing researchers and data scientists to iterate through models more quickly.
- Real-Time Inference: For applications like image recognition or natural language processing, GPUs accelerate the inference phase, enabling real-time predictions.
CUDA (Compute Unified Device Architecture)
NVIDIA's CUDA is a parallel computing platform and programming model that allows developers to use GPUs for general-purpose processing. This enables the creation of highly parallelized algorithms for AI and ML tasks.Real-Time Data Processing and Responsiveness
NVIDIA GPUs can enhance real-time data processing and responsiveness through their parallel processing capabilities and efficient handling of certain types of computational tasks.
Parallel Processing Power
GPUs are designed with a large number of cores optimized for parallel processing. This architecture allows them to handle multiple tasks simultaneously, making them well-suited for scenarios where data processing involves many parallelizable operations.
Real-Time Graphics and Visualization
GPUs excel in rendering graphics and visual elements. In real-time applications, such as data visualization or simulations, the GPU's ability to quickly render and display complex graphical data contributes to a more responsive user experience.
The Tested GPUs Set – Dedicated GPUs for Industrial Computers
Introduction to the Tested NVIDIA GPUs
NVIDIA has a large amount of GPU selections for various applications. What are their specialties, where and when can I use them, and how to choose from them? To be honest, the process of figuring out which GPU is the right one for you can be time-consuming. There’s so much to consider, from the type of PC you need to integrate GPU with, to the size of the chassis of your PC. Especially for rugged industrial computers, selecting a GPU that can operate reliably in harsh environments is challenging.
Luckily, at Premio, we offer NVIDIA GPU list that passed a comprehensive reliability, performance, and power consumption test for our latest Intel 12th/13 Generation of industrial computing products - RCO-6000-RPL, VCO-6000-RP, and KCO-2000-RPL & KCO-3000-RPL. These GPUs are NVIDIA RTX A2000, RTX A4000, T1000, 4070, and RTX 4000 SFF.
How Do These GPUs Align with the Needs of Rugged Embedded Systems?
These GPUs belong to different NVIDIA architectures and are equipped with unique designs and features. When integrated into industrial computers, each of them has a different focus.
Here's how each GPU aligns with the needs of rugged embedded systems:
NVIDIA RTX A2000
- Features:
- Compact form factor.
- Tensor Cores for AI acceleration.
- 70W TDP
- Alignment with Rugged Embedded Systems:
- Compact design suits space constraints.
- AI acceleration is beneficial for applications like image recognition in rugged environments.
NVIDIA RTX A4000
- Features:
- Mid-range GPU with a balance of performance and power efficiency.
- Ray Tracing and Tensor Cores for advanced graphics and AI.
- 140W TDP
- Alignment with Rugged Embedded Systems:
- Offers a good balance of performance for rugged applications.
- Ray Tracing and AI capabilities enhance graphics and data processing tasks.
NVIDIA T1000
- Features:
- Entry-level GPU with a focus on power efficiency.
- Suitable for mainstream professional applications.
- 50W TDP only
- Alignment with Rugged Embedded Systems:
- Power-efficient design is valuable for applications with limited power resources in rugged environments.
- Adequate performance for mainstream embedded graphics needs.
NVIDIA RTX 4070
- Features:
- High-performance GPU with Ray Tracing and AI capabilities.
- Suited for demanding graphics and computing workloads.
- 200W TDP
- Alignment with Rugged Embedded Systems:
- Provides high performance for rugged applications with demanding graphics or compute requirements.
- Ray Tracing and AI capabilities enhance visualizations and data processing.
NVIDIA RTX 4000 SFF (Small Form Factor)
- Features:
- Compact design for space-constrained environments.
- Ray Tracing and Tensor Cores for advanced graphics and AI.
- 70W TDP
- Alignment with Rugged Embedded Systems:
- Small form factor suits embedded systems with limited space.
- Advanced graphics and AI capabilities for applications like surveillance or image analysis.
Product Integration
How Do VCO-6000, KCO-2000 & KCO-3000, and RCO-6000 Incorporate These GPUs?
In our newest release featuring Intel Raptor Lake, we've integrated GPUs from the test list into key products: the VCO-6000-RPL machine vision computer, KCO-RPL Series fanned industrial computer, and the RCO-6000-RPL Fanless Industrial Computer.
VCO-6000-RPL
The VCO-6000-RPL optimizes GPU Acceleration with Dual-GPU Integration, tailored for demanding parallel processing tasks in machine vision, cloud data management, and advanced AI. With up to two PCIe x 16 expansion slots in the VCO-6000-RPL-4E with 4-slot configuration and a supplementary power source delivering up to 300W, this system is engineered to support two full-length, high-performance GPU cards, ensuring peak performance for real-time Edge AI and machine vision applications.
- Tested and Validated GPUs:
- NVIDIA T1000: 8G RAM | 896 CUDA Core | 50W | 4 x mDP
- NVIDIA RTX A2000: 12G RAM | 3328 CUDA Core | 70W | 4 x mDP
- NVIDIA RTX 4000 ADA (SFF): 20G RAM | 6144 CUDA Core | 70W | 4 x mDP
- NVIDIA4070 | 12GRAM | 5888CUDACore| 200W | 3 x DP, 1 x HDMI
KCO-2000-RPL & KCO-3000-RPL
Leveraging the latest PCIe Gen 5 standards with up to two PCIe x 16 slots supporting dual GPU, the KCO-RPL Series is compatible with a selection of dedicated GPUs to enable and process AI workloads.
- Tested and Validated GPUs:
- KCO-2000-RPL: Low-profile (Max 6.6” Length)
- NVIDIA T1000 8GB: 8G RAM | 896 CUDA Core | 50W | 4 x mDP
- NVIDIA RTX A2000: 12G RAM | 3328 CUDA Core | 70W | 4 x mDP
- NVIDIA RTX 4000 ADA (SFF): 20G RAM | 6144 CUDA Core | 70W | 4 x mDP
- KCO-2000-RPL: Full Height (Max 8.5” Length)
- NVIDIA T1000 8GB: 8G RAM | 896 CUDA Core | 50W | 4 x mDP
- NVIDIA RTX A2000: 12G RAM | 3328 CUDA Core | 70W | 4 x mDP
- NVIDIA RTX 4000 ADA (SFF): 20G RAM | 6144 CUDA Core | 70W | 4 x mDP
- NVIDIA RTX A4000 Ada: 20GB RAM | 6144 CUDA Core | 130W | 4 x mDP
RCO-6000-RPL
The RCO-6000-RPL, equipped with EDGEBoost Nodes technology, elevates real-time AI processing capabilities by seamlessly integrating PCIe Gen 4 expansions. Additionally, it offers an extra 280W power supply to support high-performance GPUs through PCIe x16 expansion.
The system is compatible with a range of robust GPU cards designed for intensive real-time Edge AI workloads.
Here are the standout options:
- NVIDIA T1000: 8G RAM | 896 CUDA Core | 50W | 4 x mDP
- NVIDIA RTX A2000: 12G RAM | 3328 CUDA Core | 70W | 4 x mDP
- NVIDIA RTX A4000 (SFF): 20G RAM | 6144 CUDA Core | 70W | 4 x mDP
Key Features of These Products That Complement NVIDIA GPUs
VCO-6000-RPL: Enhanced Power Supply
The VCO-6000-RPL comes equipped with a supplementary power source that can deliver up to 300W, catering to both the GPU card and NVMe drives, and is supported by a versatile voltage range of 12~48VDC. With its 300W power budget, the VCO-6000-RPL Machine Vision Computer can accommodate a variety of high-performance GPU cards, making it an ideal choice for optimized real-time Edge AI computing. Also, by dedicating this secondary power supply to the GPU and NVMe performance accelerators, the computer's primary power system remains free to support its advanced CPU.
VCO-6000-RPL: Sturdy Industrial Locking Brackets
The VCO-6000-RPL features durable, industrial-grade locking brackets within their PCIe expansion slots. Specifically designed to accommodate a PCIex16 GPU card measuring up to 310 mm in length and 112 mm in width, these easily adjustable brackets offer reinforced security for the GPU card and other expansion components, ensuring they remain steadfast against external shocks and vibrations.
Active Cooling and Passive Cooling
- VCO-6000-RPL
The VCO-6000-RPL provides thermal management using active cooling. With a hot-swappable smart fan and an optional secondary fan, it enhances both performance and hardware lifespan for industry-grade reliability.
- KCO-2000-RPL & KCO-3000-RPL
The KCO-RPL Series features industrial intake fans to regulate interior thermals and provide the necessary ventilation for the heat-generating components.
- RCO-6000-RPL
As a rugged fanless industrial computer, RCO-6000-RPL uses passive cooling technology for thermal regulation. With the combination of thermal paste, heatsink, and copper, the heat generated from the memory, CPU, and GPU can be dissipated, ensuring Edge AI-ready performance in extreme environments with industry-grade reliability.
Blazing-fast PCIe Gen 4 & Gen 5
Our Raptor Lake Series products utilize cutting-edge PCIe technology to provide exceptional performance acceleration. Specifically, GPUs are integrated into embedded computers via PCIe add-on expansion cards, with support for both PCIe Gen 4 and Gen 5 expansions. This ensures that our products are at the forefront of PCIe technology and able to deliver top-tier performance.
-
VCO-6000-RPL
VCO-6000-RPL-3E with 3-Slot Configuration:
- 1x PCIe x16 (16Lane, Gen4) can support one GPU
- 2x PCIe x4 [Open] (Shared PCIe 1 Lane, Gen3)
- 2x PCIe x16 (8Lane each, Gen4) can support up to two GPU
- 1x PCIe x4 [Open] (1Lane, Gen3)
-
RCO-6000-RPL
RCO-6000-RPL-2E16 has 1x PCIe x16 (Gen4), 1x PCIe x1 (Gen3)
- KCO-2000-RPL & KCO-3000-RPL
- 1x PCIe x16 Slot (Gen 5)
- 1x PCIe x16 Slot (Gen 4, 4-Lane)
- 1x PCIe x4 Slot (Gen 4, Open End)
- 1x PCIe x4 Slot (Gen 3, Open End)
Blazting-fast PCIe Gen 4 and Gen 5 provide higher data transfer rates, lower power consumption, and increased bandwidth, which allow GPUs to operate at full efficiency and effectiveness.
How Can Tested GPUs Boost Performance?
The tested NVIDIA GPUs boost performance for Raptor Lake products because of the large amount of CUDA Cores and Single-Precision Performance they brought to Raptor Lake industrial computers.
As for Single-Precision performance, we use a teraflop (TFLOP) rating to measure GPU’s performance, specifically, a teraflop refers to a processor’s capability to calculate one trillion floating-point operations per second. Saying something has “6 TFLOPS,” for example, means that its processor setup can handle 6 trillion floating-point calculations every second, on average.
Floating-point calculations are a common way of gauging the computational power of computers. So, even the low-power NVIDIA T1000 GPU can provide up to 2.5 trillion computational power every second on average, let alone NVIDIA 4070, the most power-sufficient GPU in this list. With this huge computational power brought by GPU, Premio’s RPL Series Industrial Computers can transform the future of industrial computing.
| RAM | CUDA Core | Fan | Watt |
Single-Precision |
|
| NVIDIA T1000 | 8G | 896 | YES | 50 | Up to 2.5 TFLOPS |
| NVIDIA RTX A2000 | 12G | 3328 | YES | 70 | 8.0 TFLOPS |
| NVIDIA RTX A4000 | 16G | 6144 | YES | 140 | 19.2 TFLOPS |
| NVIDIA 4070 | 12G | 5888 | YES | 200 | 29 TFLOPS |
| NVIDIA RTX 4000 SFF | 20G | 6144 | YES | 70 | 19.2 TFLOPS |
What are the specific industries and applications that benefit from these solutions?
The integration of GPUs with embedded industrial computers has ushered in a new era of enhanced performance and functionality across various key vertical markets. By harnessing the parallel processing power and specialized capabilities of GPUs, industries are experiencing notable advancements in critical applications.
Edge AI
Edge AI leverages the power of GPUs and key technologies like Sensor Fusion for on-device processing, enabling real-time decision-making at the edge of the network. This is particularly beneficial in scenarios where low latency and privacy are critical factors.
Key Applications:
- Smart Cameras: Enhancing surveillance and monitoring with real-time object detection and analysis.
- Intelligent Transportation: Powering AI algorithms for object recognition and decision-making in intelligent transportation management.
- IoT Devices: Enabling AI-driven insights on the edge for diverse IoT applications.
Industrial Automation
In industrial settings, GPUs contribute to enhanced automation and efficiency by handling complex computations and visualizations, facilitating real-time control and monitoring.
Key Applications:
- Manufacturing: Optimizing production processes through AI-driven quality control and predictive maintenance.
- Robotics: Enabling advanced vision systems for precision and adaptability in industrial robots.
- SCADA Systems: Enhancing Supervisory Control and Data Acquisition systems with powerful visualization and analytics capabilities.
Security and surveillance
GPU solutions are integral to advanced security and surveillance systems, providing robust processing power for video analytics, facial recognition, and threat detection.
Key Applications:
- Public Safety: Improving response times and situational awareness in public spaces.
- Smart Cities: Enhancing urban security through intelligent video monitoring and analysis.
- Border Control: Strengthening border security with advanced surveillance capabilities.
Medical Imaging
The healthcare industry benefits significantly from GPU solutions in medical imaging, enabling faster and more accurate diagnostics through image processing and analysis.
Key Applications:
- Diagnostic Imaging: Improving the precision of X-ray, CT, and MRI image interpretation.
- Pathology: Accelerating digital pathology workflows for faster disease diagnosis.
- Medical Research: Supporting computational tasks in medical research for drug discovery and genomics.
These key markets showcase the diverse applications of GPU solutions, from powering intelligent edge devices to revolutionizing industrial processes and contributing to advancements in security and healthcare. The adaptability and performance of GPUs make them indispensable in driving innovation across these verticals.
Summary
Premio’s industrial computers offer high-performance computing at the challenging rugged edge with a robust, modular design. By seamlessly integrating PCIe x 16 expansion slots, it allows the support for powerful GPUs, ensuring peak performance for real-time Edge AI and machine vision applications. With the booming of AI in various industries, the integration of rugged industrial computers and GPUs can contribute to the evolving landscape of AI-driven applications in challenging environments.