Edge AI


Deep Learning and Machine Learning for Edge Inference and Training

Edge AI: enabling Deep Learning and Machine Learning with High Performance Edge Computers

The number of connected devices collecting data is continually expanding. This requires more storage and computational capacity and more Artificial Intelligence (AI) to be brought at the Edge: Eurotech combines rugged embedded and Edge computers, computational power and IoT components to enable Edge AI.

By bringing these high-performance computing capacity to the Edge, Eurotech enables Artificial Intelligence (AI) applications directly on field devices. They are able to process data autonomously and perform Machine Learning (ML) in the field and apply Deep learning (DL) models and algorithms for advanced autonomous applications, such as Autonomous Driving.

The virtually unlimited capacity of the Cloud can be integrated with sophisticated and high-performance Edge Computers in the field, enabling the "Intelligent Edge". Data are processed in real-time and filtered to be sent to the cloud for additional analytics, integrating OT (operational Technology) with IT (Information Technology) applications.

The benefits of Edge AI



Assets and machines at the Edge can be trained to perform autonomous tasks

Digital twins and analytics


Digital data for real-time and remote management of devices in the field

Decision making


Real-time analytics to trigger immediate and automate decision making

Edge inference and training


Application of training models and inference happen directly on Edge devices 

Enabling Edge AI with High Performance Edge Computers

High Performance Edge Computers bring data center capabilities closer to the source of data to enable AI-powered analytics, deep learning and other data-intensive applications at the edge.

Building blocks for Edge AI

Edge Computing

Edge Computing

Computational and processing capacity are required to run Edge AI applications and algorithms directly on field devices, enabling Machine Learning and Deep Learning. The amount of data collected by assets in the field is exponentially increasing: Machine Learning and Deep Learning enable Edge AI applications for a better, real-time management of those data.  This creates Edge nodes where data can be stored, processed, filtered and then sent to the Cloud for further analytics, processing and integration with IT applications. 

Support for advanced Edge Analytics

Industrial Automation and Edge AI applications require real-time decision making. Therefore, data analytics must be performed at the Edge to provide immediate response to critical issues. Eurotech simplifies Edge analytics and data management on field-deployed IoT gateways and computers with Everyware Software Framework (ESF). The IoT Edge Framework provides a user-friendly and simplified application development environment to digitalize assets and manage their digital twins for advanced analytics and data management


Edge Analytics
Autonomous Driving Challenges

Edge Inference and Edge Training

The expectations from devices at the Edge are ever growing: the amount and quality of data are exponentially increasing and the need to process them autonomously is mandatory for some applications. An example of an Edge AI application is Autonomous Driving, that relies on Terabytes of data coming from HD cameras, RADARS, LIDARS and other high-speed sensors that must be processed within short fractions of time. The capacity to enable Deep Learning algorithms to perform Edge Inference and Training directly on-board is a must-have feature for these kind of applications in Automotive and Industrial environments. 

Edge AI: related stories

AI-ready Edge Computers: IoT for Deep Learning and Machine Learning

Edge Artificial Intelligent networks feature hardware and software platforms connected with IoT technologies. Eurotech provides a set of Edge Computers, Multi-service IoT Edge Gateways and IoT Edge Servers that have the computational capabilities to support advanced Machine learning and Deep Learning applications. 

White paper: High Performance Edge Computing

Edge computing is the new domain for innovation. Next-generation analytics, machine learning (ML), and other high-performance workload processing require comprehensive intelligent edge frameworks and platforms. Furthermore, these platforms must have maintained pace with the convergence of embedded/OT and enterprise/IT domains through the past several years. High Performance Edge Computing features distinct development requirements and challenges spanning hardware, software, connectivity, platform integrations, and security.


High Performance Edge Computing White Paper

How to enable Deep Learning with High Performance Embedded Computers (HPEC)

Eurotech provides High Performance Embedded Computing (HPEC) systems and switches to manage huge steams of data at the Edge at an unprecedented pace. They provide tremendous computational, storage and bandwidth capacity to support Deep Learning and high-performance algorithms to enable Autonomous Driving and other Edge AI applications. 

HPEC Autonomous Driving White Paper

White Paper: Data Logging for Autonomous Driving

Autonomous Driving is classified according to the amount of human driver intervention and ranges from Level 0 (no automation) up to Level 5 (full automation). Enabling Level 5 Autonomy in Automotive, Defense and other industries requires collecting, storing and processing data at an unprecedented degree, which has been until now unattainable by Embedded Devices and Edge Computers.


Rugged HPEC systems designed to bring AI into embedded applications


Ultra-high computational performance that goes beyond the traditional embedded computer capabilities


Advanced storage options and capabilities to manage the huge amounts of data of HPEC applications


40/56/100 GbE ports to support constant, intense data streams


More computational density and better energy efficiency


Reliable, continuous operations for long period of time in very harsh environments


Designed to fit into embedded applications where space is at a premium

Autonomous Driving challenges

Blog post: the challenges of Level 5 Autonomous Driving

Companies in the Automotive industry are spending billions of dollars in investments for developing Level 5 Autonomous Driving technologies. All these players are encountering a number of new challenges that span across many disciplines and technologies.


Edge Computing and AI: latest news

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