What Is Edge Computing?

Edge computing brings compute power and storage closer to the edge of a network where data generation occurs. Moving compute power, storage, and connectivity closer to the source of data generation delivers substantial business benefits, including real-time data analysis and decision making, a foundation for machine intelligence. By eliminating issues associated with latency and increasing the amount of bandwidth near IoT sensors, enterprise businesses can achieve better productivity and automation across operation technology (OT) to information technology (IT) processes.

How Edge Compute Works

Edge Computing models enable symbiotic relationships between purpose-built hardware and software working together for interoperability into millions of connected IoT devices, large limitless data sets, and cognitive situational awareness through smart sensors and machine learning algorithms trained for AI.

Device Data

The explosion of IoT technology and sensors help generate incredible amounts of data to help inform better machine learning and intelligent modeling.

Intelligent Insights

Real-time data access and insights are enabling the edge for more intelligence and automation where inference analysis can provide real-time processing

Strategic Actions

Strategic actions based on data and insights improve costs, mitigate risks, and improve productivity and efficiency with greater agility.

Resource-Intensive Training of algorithms can be done in the cloud and then shared out to the edge where lighter inference capabilities can quickly act on data.

- Deloitte Insights

Why is Edge Computing Increasing In Importance?

Edge computing devices are becoming more important thanks to the explosion of data generated from the explosion of IoT devices.
As sensory data increases every year, edge computing allows the data to be processed in real-time, resulting in faster decision-making, faster response times, and improved automation when it matters most.

Key Industry Trends

  • Less Reliance on the cloud but better workload utilization for real-time intelligence
  • Creates more autonomous control loops for IT to OT productivity and efficiency for cyberphysical infrastructure
  • Localized processing in close proximity to data, provides machine learning through inference analysis and real-time cognitive awareness
  • Ultra-low latency use cases and immediate response times with advanced connectivity technologies (5G and Wifi 6)
  • Achieve greater automation and better business decisions with machine intelligence

Why Does Edge Computing Need to be Rugged?

Location, Location, Location...

Rugged edge computing is backed by our industrial-grade designs that have endured rigorous environmental testing and validation in certified test labs. Our rugged edge computers help bring processing power, storage, and connectivity closer to the source of data generation in the most volatile IoT deployments today. Modern day computing hardware for scalable edge computing needs to be small, rugged, power-efficient, and performance driven. Many new edge computing applications are being deployed in rugged, remote, and mobile locations requiring the ruggedization of the hardware for mission-critical reliability.

Dust & Debris

The fanless design of rugged edge computers allows for passive cooling of the system, making edge computing solutions excellent for handling deployment in environments where they will be exposed to dust, debris, humidity, and extreme temperatures

Extreme Temperature

Rugged edge PCs are engineered and built using wide temperature range components, creating a system that's very capable of being deployed in environments that experience extremely cold and extremely hot temperatures, ranging from -40°C to 85°C.

Shock & Vibration

Rugged edge computers employ a cableless design, utilize SSDs (solid-state drives), and have reduced the number of joints, allowing them to withstand exposure to frequent shocks and vibrations in compliance with the MIL-STD-810G.

Low Power Consumption

Edge computing hardware utilizes powerful yet energy-efficient processors that consume little power and produce even less heat, making them ideal for deployment in remote locations. These systems feature over-voltage, over-current, and reverse polarity protection to prevent any damage from unstable power supply.

Small Footprint

Rugged computers utilize a compact design, giving them a small footprint. The small footprint permits deployment in space-constrained environments. With different mounting options like wall mount and DIN rail mount, rugged edge computer can be installed quickly and easily.

Benefits Of Rugged Edge Computing

Bridging the IoT Data

Sensor data from IoT endpoints will continue to increase everyday with millions of devices coming online everyday. Computing resources shifted closer to data generation provide real-time insights.

Performance Increase

Ultra-Low Latency

Rugged edge computers offer ultra-low latency compute power at the edge by tapping into the latest advanced wireless technologies in 5G and Wifi 6 networks, allowing for real-time data telemetry for the most critical workloads.

Reduce Network Traffic

Reduce Bandwidth Usage

Edge computing solutions reduce the amount of required internet bandwidth since they process data locally at the source of data generation, only sending post-processed data to the cloud for remote monitoring and control.

Cost Saving

Internet traffic bandwidth, cloud computing services, and data processing duration are all high cost that can be greatly reduced with edge computing. In addition, rugged edge computers can save energy cost by utilizing power efficient processors.

Reduce Cost

Better Reliability

In any event that cloud computing is interrupted, disconnected, or unavailable, rugged edge computers still can service. Any damages and errors from IoT devices can be detected for immediate actions.

Enhanced Security

TPM 2.0 secures rugged edge computing hardware from being tampered with. With less data has to be sent to the cloud for processing, this reduce the possibility of packages being lost and targeted.

Enterprise AI and Machine learning models require a blend of versatile hardware and dedicated software to meet each unique intelligence need. With leading-edge computing, AI solutions are thoroughly trained, optimized and deployed for immediate efficiency for real-time insights.

Why Choose Premio For Rugged Edge Computers

Expertise In The Design, Engineering and Manufacturing for Edge Computers for industry 4.0 automation and rugged IoT deployments in remote and mobile applications

  • 30+ years of extensive design expertise in rugged computing solutions focused for reliability and longevity
  • Thermal simulation chambers to guarantee wide operating temperatures remote and mobile edge deployments
  • Global turnkey manufacturing and support infrastructure to accelerate scalable mass deployments in rugged edge computing solutions
  • Long Product Life Cycles to ensure hardware reliability
  • Deep understanding of IoT technologies in computation, storage, and connectivity designed for edge computing
  • Regulatory testing and compliance options for rugged edge computers in the North America Markets

Our Partners

Premio is part of Intel’s Parner Alliance Program that provides special access to semiconductor silicon solutions and powerful processing technology. As Titanium members Premio designs and manufacture system level computing solutions from the edge to the cloud.
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Premio is part of Nvidia Partner Network that provides in-depth knowledge into the latest technologies powering machine learning and artificial intelligence. Our rugged edge computing solutions are validated to support enterprise performance acceleration cards (GPUs) from Nvidia’s portfolio of compute, virtualization, and visualization products.
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Premio’s edge computers are certified to run AWS IoT Greengrass for developers requiring a less centralized processing platform for their IoT network. AWS IoT Greengrass empowers edge computers to act locally on the data they collect and generate.
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Premio is part of Hailo’s hardware partner ecosystem that enables technology leaders to bring advanced deep learning capabilities to a variety of edge computers with domain-specific performance acceleration.


Rugged edge computing is used to enable applications that require real-time, low latency data processing and storage in volatile environments that are not friendly to regular desktop computers.

An example of edge computing includes deploying an edge computer in a desert to monitor and control oil production machinery and equipment. An edge computer is one that’s deployed in close proximity to the source of data generation where people must make decisions. In this example, the edge computer would gather information from the oil production machinery, and process and analyze it in real-time, enabling operators to make quick decisions. Additionally, edge computers can be used for remote monitoring and control of oil and gas production assets.

Another application that requires real-time low latency data processing is autonomous vehicles, where edge computers must process, analyze, and make decisions in as little as a second millisecond.

Rugged edge computing matters because it brings real-time, low latency compute and storage power closer to the source of data generation in harsh environments that regular, consumer-grade desktop PCs cannot survive. Some applications, such as autonomous vehicles, remote monitoring and control of machinery and equipment, surveillance and security, and factory automation. Edge computing matters because it enables these applications that would not otherwise be possible without edge computing. These applications often require real-time data process and analysis, relying on the cloud is not possible because the time it takes data to travel thousands of miles from the origin device to the cloud and back often takes a few seconds, which is simply too long for applications that require real-time decision making. Furthermore, edge computing matters because it alleviates the burden placed on the cloud as the number data-generating IoT devices continues to increase.

  • Small, power-efficient compute architectures both is multi-core CPU and GPU performance driven accelerators
  • Faster memory and computational storage
  • Wireless 5G Networks for sub millisecond data processing for lower latency, more bandwidth and faster connectivity in real-time
  • Streamline balance for workload consolidation and localization of data processing specific to application

Although edge computing is not new since some forms of edge computers were used to store, process, and distribute data since the mid 1990s. However, recently, there has been a noticeable growth in the number of edge computers being deployed, and the number is only expected to grow as 5G technology is rolled out. Industry leading analysts estimate that the edge computing market will be worth of $43 Billion by 2027.

Edge computing is not replacing cloud computing, instead edge computing compliments and improves cloud computing. Edge computing reduces the burden placed on the cloud by storing, processing, and analyzing data on edge computer, only sending data that sets off certain triggers to the cloud for remote monitoring and analysis. This alleviates the burden placed on the cloud for continual increase in the number of IoT devices. Major enterprise cloud companies like Amazon’s AWS offer dashboard management for device control and connectivity from the cloud.


Cloud computing involves the delivery of computing services, such as storage and computing power over the internet, allowing users to access services using their own computer from anywhere around the world without having to invest in the infrastructure required to store and process their data.


On the other hand, edge computing is more decentralized and involves the deployment of edge computing devices at the edges of a network. The edge computing devices are placed close to the source of data generation to process data locally. Edge computing has enabled mobile computing as well as IoT technologies.

  1. Ubiquitous Intelligence (IoT + Cloud + AI) - Modeling training and inferencing workloads disaggregated and distribute across new AI applications accessible by data insights
  2. Edge Cloud Computing (5G + Cloud) - Base stations and cloud nodes communicating with endpoints for data but also still in proximity for necessary compute power and ultra reliable latency. Balance workload dedicated to the mission-crtical applications
  3. Autonomous Infrastructure – (5G +AI) Autonomy is a means to scale economically and operationally. Fully intelligent automation can reduce cost, mitigate risk, and prevent unwanted downtime.

Achieving real-time decision making and predictive analytics is an increasingly strategic goal among industrial operations – an imperative fueled by rapid digital transformation and a growing appetite for automation upgrades across the broad spectrum of commercial and manufacturing applications. Rugged edge computing plays a critical role in this landscape, accelerating data processing based on a variety of sensor input data and enabling access and analytics close to the data source. For example, the primary goal of many new IoT applications is the delivery of a level of intelligence refined beyond human capabilities or pace. In these applications, machine learning is required but must be supported by dedicated hardware to process and run algorithms effectively.