Category: Edge AI

Gun-related violence presents an escalating threat to public safety, emphasizing the urgent need for faster detection and response to minimize casualties. Traditional security systems reliant on human monitoring or reactive alerts often face delays in response times, increasing the risk of harm.

Category: SD-WAN

To enhance secure, resilient, and efficient communication between remote ATMs, bank branches, and headquarters in the era of digital banking. As financial organizations increasingly adopt advanced self-service and digital solutions, deploying a robust SD-WAN architecture using uCPE (Universal Customer Premises Equipment) is essential for maintaining reliable end-to-end connectivity across distributed locations.

Category: Network Computing

In today’s AI-driven landscape, modern AI infrastructure faces several core challenges: handling massive data volumes, managing high-speed GPU-accelerated computations, and ensuring low-latency networking across AI resources. These  infrastructures must support parallel processing for demanding tasks like AI training and inferencing, necessitating an advanced, high-throughput network to deliver seamless, secure, and efficient AI workloads.

Category: Edge AI

As 5G networks evolve, they enable ultra-low latency, faster speeds, and massive device connectivity. However, traditional CPU-based systems have struggled to handle computationally intensive virtualized Radio Access Network (vRAN) functions, creating bottlenecks in performance. To address these challenges, integrating AI at the edge—where data processing is closer to the source—has become essential. AI-accelerated infrastructure optimizes latency and bandwidth usage while supporting critical network requirements such as software-programmable Network Operating Systems (NOS) for RAN operations.

Category: Edge AI

With the growing reliance on Generative AI and Large Language Models (LLMs) across industries, organizations are increasingly focusing on secure, high-performance, and cost-effective AI solutions. However, centralizing LLM training and inferencing in the cloud introduces challenges, including data privacy risks, high transmission costs, latency issues, and dependence on constant cloud communication. The demand for edge-based AI infrastructure is rising as enterprises seek to harness AI capabilities while maintaining control over sensitive data.

Category: Edge AI

Lanner has deployed its Edge AI appliances, the LEC-2290E and EAI-I731, at its manufacturing facility in Taipei, Taiwan, to upgrade the Automated Optical Inspection (AOI) system on its Surface Mount Technology (SMT) production line.

Category: Network Computing

AI-driven threat detection plays a crucial role in today’s OT/IT cybersecurity. These network security measures are capable of rapidly processing large datasets in order to detect patterns and anomalies that indicate potential security breaches. Machine learning algorithms, for instance,  can spot unusual traffic patterns that point to a likely DDoS attack.

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