Shaping the Future of Rugged Edge AI with Hardware Acceleration 


As the world becomes increasingly more connected, the demand for real-time data processing and analysis has skyrocketed. Edge AI and edge computing have emerged as the next technology that is seeking to bring a new era in the productivity revolution.

“75% of data will be processed outside the cloud by 2025 - Gartner  

If you have followed a lot of our content, this is something that has been mentioned throughout all our materials. This statistic from research consulting firm, Gartner, is referenced many times throughout several content pieces and is something that continues to usher in a wave of digital transformation for many enterprise businesses as they enter an industry 4.0 era. Through this transformation, businesses and organizations are bringing processing much closer to the edge, next to the points of data generation, to process and analyze data that benefits from lower latency, better bandwidth, and more efficiency. These points are changing the way we rely on data and allow for a wide range of new applications we see today, including autonomous vehicles, intelligent manufacturing, automation and more. However, the greatest disruption and change is the introduction of advanced machine learning and artificial intelligence capabilities to devices at the edge of the network for real-time analytics. Gartner’s prediction is an indication for businesses to embrace edge computing technologies in order to stay competitive in today’s data driven world where data is becoming more valuable than gold. 

What is Edge AI? Edge Computing? 

Edge computing and edge AI are two rapidly emerging technologies that are transforming the way we process and analyze data. In today's world, data is becoming increasingly critical to business operations and edge computing/AI are being designed as tools to help organizations process and analyze data in real-time. 

Edge computing is a decentralized computing architecture that enables data processing and analysis at the edge of the network, closer to the source of data. This technology has gained popularity due to its ability to reduce network latency, increase efficiency, and improve security. While on the other hand, Edge AI is the application of artificial intelligence (AI) algorithms and models in edge computing devices. It allows devices to make intelligent decisions on their own, without relying on cloud computing models. This means that data is processed locally, thus eliminating the need for data to travel long distances. 

However, this doesn’t mean cloud computing is entirely excluded. Rather, edge computing forms a symbiotic relationship with cloud computing. Both are complementary technologies that work together to provide even further efficiency in data processing and analytics. Together, these two create a powerful architecture, “Cloud to Edge” infrastructures, that enable businesses to scale efficiently with more productivity and real-time data. This architecture map can be divided into 4 distinct paradigms from Cloud to the Edge. Created by the Linux Foundation Edge community (LF Edge), this taxonomy provides a thorough framework of the cloud to edge ecosystem for rugged edge computing and helps expand on key areas in edge connectivity. 

The Four Main Paradigms of Cloud to Edge

(Source: Linux Foundation Edge)

When looking from a top-down overview of the Cloud to the Edge, each specific paradigm presents an attribute which brings a different level of processing power the further we step away from the centralized Cloud data centers. the choice of cloud to edge data processing paradigm will depend on the specific needs of an organization, such as processing requirements, latency requirements, data security needs, and budget constraints. Organizations can choose to adopt one of the four paradigms, or a combination of multiple paradigms based on their needs. 

Datacenter Edge Cloud  

Also known as the Service provider edge, this area borrows heavily from traditional data centers, their tools and practices. Think of these as smaller scale data centers that are placed at the immediate edge, the first step away from the Cloud. Datacenter edge cloud are positioned just as securely to the cloud and offer a physically in a well-defined network perimeter that allows for a consistent connection.  

Distributed Edge Cloud 

The next step away from the Cloud in the immediate edge is the distributed edge cloud. These are centric edges that are deployed in smart device edge, located outside of traditional data centers. These smart edge devices are capable of supporting cloud architectures and can span from a gateway device on a truck to a cluster of servers in a factory or retail store.  

End User Edge Cloud 

Many people may be very familiar at this level due to the devices that are used at this physical location of the Edge to Cloud. These devices are end user devices that are found all over the world. From smartphones to desktop computers to tablets, these UI centric devices are established ecosystems that revolve around Windows, Android, and IOS. 

Constrained Edge Cloud 

On the very far end of the paradigms, lies the constrained edge cloud. This paradigm is characterized by lightweight devices and sensors in the physical world that leverage microcontrollers and perform basic functions. These devices typically require highly customized device management and security tools due to the resource constraints at this level.  

For a deeper dive into how Premio computing solutions fit into the edge computing paradigms, take a listen to our podcast episode, "Connecting The ‘Near Edge’ & ‘Far Edge’ With Dedicated Computing Hardware"

While it varies from organization to organization which paradigm they fall under and adopt, it is important to note three driving technologies that are driving a convergence between these paradigms and the Rugged Edge: Cloud computing, 5G low latency communications, and Artificial Intelligence. 

Relying solely entirely on Cloud computing has become less feasible as these driving technologies continue to grow. The growth opens the door for new workloads right where data is being generated, paving the way for automation in a multitude of industries.

Macro Trends at the Edge 

The paradigms from the Cloud to the Edge are also driven by further macro trends that we see being distributed from both ends. With the rise of the rugged edge and edge deployments, it is very important to note that Cloud is not going to disappear, but rather, a swing towards decentralized computing is at play. We are simply going to be seeing a much wider distribution of computing resources from the cloud to the edge, rather than a solidified preference over one or the other.  

Cloud-native Expanding to the Edge  

As previously mentioned, the rugged edge doesn’t alienate away from the Cloud. Rather, they take principles from it and form a symbiotic relationship with the cloud to create a solution that utilizes both resources in some shape. There are a variety of different ways the rugged and the cloud come together to work in harmony. These include:  

  • Cloud centric – The cloud is the central data storage and scalable computing solution that works with edge applications that collect and preprocess data.  
  • Cloud support – Cloud infrastructure is used as central training grounds for many AI/ML models, that will be deployed at the edge, where the majority of the data is collected and maintained.  
  • Edge Centric – Centralized data now resides on premises where the data is generated, with only certain functions (such as remote orchestration) being performed from the cloud.  
  • Edge Native – A newer coined term that has emerged that uses cloud native principles with edge centric operations in mind. The solution is fully tailored to prioritize on location needs.  

Open-source Driving Standards  

Rugged edge computing’s benefits are widely known and proven to greatly boost many applications. However, deploying edge solutions that are reliable and can support mission critical data is complex, requiring a deep complex mix of hardware and software. Because rugged edge computing seeks to bridge the physical world and the digital world, standardization and interoperability is extremely important. 

Open-source software and collaboration are imperative to help drive standardization in modern technology. Through open-source APIs, these shared technologies make it easier to help spread coding of components across different developers, a clear principle in the cloud computing era.   

For example – open-source AI models for training and application building have become widely popular machine learning libraries for many developers to build, contribute, and use throughout many various AI applications.

is one such
open-source machine learning library that was created by Google. It allows developers to easily build and deploy machine learning models for a variety of tasks, such as image recognition, natural language processing, and predictive analytics. The core component of TensorFlow is its computation graph, which is a representation of the mathematical operations that make up a machine learning model. The graph can be executed on a variety of computing platforms, including CPUs, GPUs, and TPUs. TensorFlow also provides a high-level API for building machine learning models, as well as lower-level APIs for more fine-grained control. TensorFlow has become one of the most popular machine learning libraries in the world, with a large community of developers and researchers contributing to its development and usage.

(Source: “You Only Look Once: Unified, Real-Time Object Detection” Paper)

Other open-source machine learning libraries that are popular are: YOLO Object detection and RESNET 50. Both models are computer vision models that are highly popular for building computer vision applications for object/image detection.

OT/IT Convergence  

Organizations will either expand from traditional operational tech systems or down from traditional information tech systems (data center/cloud) to approach the edge. For many organizations, this is called “OT Up” or “IT Down” and brings specialized considerations.  

Both approaches are not mutually exclusive and strike a balance as they move toward each other. OT Up is rooted in industrial processes – IoT solutions enabling operations. The rugged edge. They leverage lighter gateway hardware and sensors to modernize edge infrastructure. IT Down is rooted cloud computing and comfortable data centers. As they approach the edge, they extend data center practices to the real world, but maintain centralized control.  

Linux in the Industrial World 

As open-source software allows for collaboration and drives standardization, the concept is about freedom and time to market. Through sharing services, resources can be applied higher up the value chain while ensuring interoperability.  

Data Trust 

With AI solutions driving more automation and data generation, the opportunities are endless. We are seeing this exact rise in the number of AI tools that are being created today. As with any accelerated growth, the risk of false data rises, which calls for and necessitates for more measures in security and trust.  

For example, IEC 62443 is a series of international standards that provide guidelines and best practices for industrial automation and control systems (IACS) security. These standards were developed by the International Electrotechnical Commission (IEC) and aim to provide a comprehensive framework for securing IACS against cybersecurity threats. The IEC 62443 standards cover various aspects of cybersecurity, including risk assessment, security policies, procedures and guidelines for secure development, testing and maintenance of IACS systems, network security, access control, and incident response. The goal of IEC 62443 is to provide a standardized approach to securing industrial automation and control systems, which are often connected to enterprise IT networks and can be vulnerable to cyber-attacks that could result in production disruptions, equipment damage, and even endanger human lives. 

Ruggedized Industrial Computers for Rugged Edge AI

As mentioned before, Gartner’s prediction that 75% of data generated will be processed at the edge by 2025 can be further supported through their recent Emerging Technologies and Trends Impact Radar for 2023. They have recognized that “Edge AI” and “Edge Computer Vision” are on the cusp of explosive growth. The ecosystem of edge computers has taken a strong foothold in shaping Industry 4.0’s productivity revolution, and rugged edge computing will continue to play an integral role in helping shape this trend. 

(Source: Gartner)

What is Rugged Edge Computing?

While Edge AI takes a crowning role in the spotlight in Industry 4.0, what truly enables Edge AI applications and allows AI algorithm and machine learning tools to perform are industrial rugged edge computers.  

These rugged edge computers play a mission critical role in implementing and allowing Edge AI to ultimately function. They are the physical infrastructure for rugged AI deployments by providing not only the necessary computing power but also the dense data storage needed to process the large amounts of data that is generated in edge AI applications.  

As previously mentioned, Edge computers are designed to function independently away from cloud centric data centers and reside directly next to the point of data generation. However, edge computers are special in the sense that they are hardened with ruggedization and reliability in mind. As IoT devices and data continues to grow, there are essential hardware requirements that must be met to ensure rugged edge computing and edge AI applications operate as smoothly as possible.  

Industrial rugged edge computers are specifically engineered and built to withstand deployment in volatile environments. They are built with a high level of durability through ruggedized features and design. Everything from the external housing to the internal components is tested and validated to run reliably in the most volatile environments.   

Not only must rugged edge computers withstand harsh environments, but they must also meet application performance requirements to perform tasks and workloads without failure and prevent any jeopardization of data in mission critical deployments. Especially for mission-critical deployments at the edge, there are specific computer hardware requirements that can benefit ultimate reliability  

Where Rugged Edge Computers fit 

Inflection points are moments in time when key transformative technologies disrupt the status quo and fundamentally change the way we live, work, and interact with the world around us. The advent of the internet, smartphones, artificial intelligence all represent some form of inflection point that is a key technology that shaped the world. In rugged edge AI and rugged edge computing, there are many key inflection points that have helped evolve and transform it for Industry 4.0. These points include:  

  • Proliferation and adoption of IoT sensors
    • The growing use of connected IoT sensors and devices that are embedded in a variety of devices & the ability to collect enormous amounts of data. 
  • Ubiquitous Compute
    • Ability for technology to integrate into our everyday lives and part of our routine. These technologies paved the way for new applications and services, enabling them to collect and transmit data and communicate with one another.  
  • Cloud-to-Edge Infrastructure
    • A symbiotic relationship, but a shift away from the cloud to a decentralized edge infrastructure  
  • Pervasive Connectivity
    • Increases in IoT sensors and devices 
    • Bringing together diverse tools by allowing them to share data processes – wireless connectivity: Wi-Fi, 5G/4GLTE 
  • Artificial Intelligence
    • Open-source AI models and machine learning advancements that are now able to perform tasks that typically require human interaction, leading to significant breakthroughs in industrial applications such as manufacturing, healthcare, and much more 

When we consider each of these inflection points, they factor into the whole solution that is rugged edge computing. Each technology advent has created a pathway for endless possibilities for new levels of performance in data analysis. 

Rugged Edge Computing in the Physical World

Premio has been a major part of the Rugged Edge ecosystem, playing an integral role in the framework of industrial grade computing solutions for over three decades. Various trends in a multitude of industries have emerged within the architecture of Cloud to Edge, and positioning specific computing solutions in key market segments helps position Premio for the future. Security and Surveillance, Industrial Automation, and Autonomous intelligent transportation are just a few vertical segments that Premio focuses its attention on with its portfolio of industrial grade computing. In each of these verticals, Premio provides robust engineering expertise that results in a complete portfolio of x86 computing designs for rugged edge applications. 

Security & Surveillance 

Traditional surveillance applications have always heavily relied on data but what is now shaping new opportunities for this market is the ability to use video analytics with AI directly to process data in real-time. New sensor technologies and high-definition cameras are pushing the envelope of multiple video streams for new frameworks of processing and analytics. The advances in AI and machine learning are applied to create applications that allow for object detection, facial recognition, and much more with incredible speed and accuracy. 

These new security and surveillance applications are made possible through the latest transformative technologies that rugged edge computing has adopted. The various inflection points of computing infrastructure, proliferation of IoT devices, and artificial intelligence become the driving force in shaping today’s security solutions – and it is through these trends a major shift of rugged edge computing power is made available through powerful NVRs (Network Video Recorders) and AI Edge Inference Computers. This ultimately creates new demands for more robust computing solutions to process, store, and analyze the data in real time.

Learn more about security and surveillance 

Industrial Automation 

Industrial automation and manufacturing have been undergoing a digital transformation in recent years, as edge computing and edge AI analytics are increasingly being deployed to make these processes more automated and efficient. For example, factory floors and manufacturing plants have seen an explosive growth in the use of IoT sensors, robotics, and computer vision to power automation in warehouse facilities. Edge computing refers to the practice of processing data locally on devices at the edge of a network, rather than in a centralized location. By using edge computing and edge AI analytics, manufacturers can analyze data from sensors and devices in real-time, allowing them to make more informed decisions and optimize their production processes. This includes using machine learning algorithms to detect patterns and anomalies in data, enabling predictive maintenance and reducing downtime. Edge computing and edge AI analytics are also being used to optimize logistics and supply chain management, as well as to monitor worker safety and productivity. As these technologies continue to advance, we can expect to see even more automation and efficiency in industrial automation and manufacturing. At the core of this digital transformation for automation are reliable and proven computing solutions that balance the latest transformative technologies in processing, memory, storage, and wireless connectivity. 

Learn more about industrial automation

Autonomous Vehicles and Intelligent Transportation 

Autonomous vehicles and intelligent transportation systems are rapidly evolving with the help of edge computing and edge AI analytics. Edge computing allows for real-time processing of data collected by sensors and cameras on vehicles and at roadside infrastructure, enabling quick decision-making for the vehicle or the transportation system. Edge AI analytics can analyze this data to identify patterns, detect objects, and make predictions about future events. This can include everything from detecting and avoiding obstacles on the road to predicting traffic patterns and optimizing transportation routes. By utilizing edge computing and edge AI analytics, autonomous vehicles can make decisions in real-time, reducing the risk of accidents and improving overall safety. Additionally, intelligent transportation systems can optimize traffic flow and reduce congestion, making transportation more efficient and reducing emissions. As these technologies continue to evolve, we can expect to see even more automation and innovation in the transportation industry. 

Learn more about intelligence transportation

How Premio Fits Into An Expanding Edge Ecosystem 

Premio’s 30+ years of engineering in computing solutions has put us right within the ecosystem of edge AI and enterprise computing. As the convergence of AI, 5G, and cloud continue to shape the rugged edge, it opens many avenues for new workloads within industrial automation, intelligence transportation, security/surveillance, and much more. These demands will all require purpose-built hardware that enables compute power and performance accelerations in these real-time, mission critical applications. 

As a leading provider of industrial grade edge computing solutions, our expertise allows us to bring ultimate performance and reliability in the most remote and harshest environments. Our portfolio of rugged edge computers is designed with key engineering principles and transformative technologies for reliable x86 computing solutions to shoulder mission critical workloads. Through a fanless and cableless design, Premio offers ruggedized computing solutions that range from low-power processing for telemetry to high performance inferencing. Our rugged edge computers are built rugged and ready to meet the growing market demands from major trends in the ecosystem of IoT, edge and cloud computing.