Eagle-Lanner tech blog

 

Artificial intelligence (AI) is transforming the way we interact with technology and industries across the board, and edge AI is no exception. Edge AI refers to AI algorithms and models that run on devices or hardware at the edge of a network, as opposed to being processed in the cloud or on a centralized server. This approach reduces latency, conserves bandwidth, and increases privacy by keeping data local. From autonomous vehicles to intelligent drones and robots, the demand for high-performance AI processing solutions continues to grow. One such solution is Nvidia’s Jetson family of AI-enabled edge devices, which includes the Jetson Xavier NX, Jetson Orin NX, and Jetson Orin Nano. Let’s explore and compare these devices, focusing on their AI capabilities and processing power.

As 5G networks continue to roll out across the globe, the demand for high-speed connectivity has continued to escalate. This surge in demand for agile, high-performance, and scalable networks has led to an increase in the deployment of virtual radio access networks (vRANs), which separate the hardware and software components of a mobile network. To enhance the performance of vRANs, Intel has introduced the Intel vRAN Boost solution. Another factor expected to drive the adoption of Intel vRAN Boost is an increase in complex applications beyond mobile broadband that require different 5G network behavior.

The new connected world requires wireless connectivity everywhere you go – whether it is at home, on the go, or at work. The rapid increase in the volume of data, plus the need to process said data in multiple places – at the endpoint, the edge, and in the cloud – has created a challenge and made faster movement of data a priority. We have discussed previously how Open RAN provides Communications Service Providers (CSPs) more choice and flexibility to efficiently and cost-effectively deploy their radio networks, let's further explore the abilities of accelerator cards.

Nowadays, private 5G is not only for telecommunication networks, it is an integrated solution including networks, cloud computing, edge computing, and application platforms. For vertical industries, deploying both a private 5G network and a multi-access edge computing (MEC) solution is a suitable option to accelerate the process of digital transformation. Private 5G network provides security, speed, scalability, and stability required to process data efficiently. Edge computing further supports latency-sensitive industrial applications enabling time-sensitive data to be processed quickly at the edge.

The railway industry has been an essential part, used as a means of transportation for passengers or to carry freight, of every country in the world, some call it the economic backbone of every country. When we talk about digital transformation in the railway sector, we usually think of e-ticket machines, passenger information displays, and onboard connectivity aka passenger Wi-Fi, however technological advancements in the rail industry have increased system interconnections, signaling systems, train control systems, and telemetry.

Automotive Ethernet is a new physical layer standard, adapted and meets the needs of automotive use cases, including meeting electrical requirements (EMI/RFI emissions and susceptibility), bandwidth requirements, latency requirements, synchronization, and network management requirements. It enables high baud rate communications and offers high immunity, reduced cabling, and high-speed data rates, capable of automotive electromagnetic compatibility and immunity requirements in automotive conditions.

The increased proliferation of the Internet of Things (IoT) continues to evolve and drive the promise of a connected world, from connected factories to connected office spaces to connected vehicles. Connecting vehicles continues to grow rapidly, and generates an enormous volume of data, and when this data is harnessed and analyzed, it presents a remarkable opportunity to make vehicles more efficient and roads safer, but one of the challenges is still limited to network capacity. A possible solution is multi-access edge computing (MEC), which can lower latency, offering massive bandwidth for processing and improves data transmission between vehicles and road infrastructure, reducing lag for critical decision making.

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