Events & News

The Internet of Things (IoT) is changing our lives quickly. It connects billions of devices in smart homes and industries. However, this growth poses challenges for traditional cloud computing, including bandwidth limitations, latency issues, and data privacy concerns. Edge computing solves the problem by processing data closer to its source. This reduces delays and dependence […]

From ChatGPT and DeepSeek to autonomous vehicles and intelligent security systems, artificial intelligence has rapidly evolved into a cornerstone of digital innovation. But while breakthroughs in algorithms get the spotlight, the reality is that these models rely on equally powerful hardware to run efficiently. Traditional CPUs, even the most powerful ones, struggle to handle the

The AI Landscape Is Evolving—Fast Over the past decade, AI has largely been powered by the cloud. Complex models were trained and deployed in massive data centers, relying on constant connectivity and remote compute power. But that’s changing rapidly. Edge AI—where intelligence is embedded directly in devices at the edge of the network—is gaining serious

From large language models like ChatGPT to autonomous vehicles, generative AI, and high-resolution video analytics, AI is transforming nearly every industry at breakneck speed. But behind every intelligent model lies a hidden challenge — the ability to feed massive amounts of data into these AI systems quickly and reliably. As AI models grow from millions

Edge computing involves processing data right at the source where it’s generated, rather than transmitting it to a distant central server or data center. It’s akin to having a local mini-office sort your mail on the spot, instead of shipping it off to a large, central hub far away. This approach helps streamline the entire

With the deep integration of artificial intelligence and IoT technologies, the AIoT field is ushering in unprecedented development opportunities. As an industry leader, Rockchip held the 8th Rockchip Developer Conference (RKDC2024) from March 7 to 8, 2024 in Fuzhou. With the theme of “AI Chip – AI Application – AloT”, the conference focused on the

As industries demand faster, more secure, and reliable data processing, the convergence of edge computing and artificial intelligence (AI) is reshaping how we interact with technology. By moving computational power closer to data sources, edge computing eliminates the bottlenecks of centralized cloud systems, enabling real-time insights and autonomous decision-making. This article explores the transformative potential of AI on the Edge,

Tom’s Hardware
The review provides a detailed comparison between the Geniatech XPI-3566-Zero and the Raspberry Pi Zero 2 W. The Geniatech board offers more RAM and storage options, making it a compelling alternative. It features a quad-core Arm Cortex-A55 CPU with multiple RAM configurations and eMMC storage capacities.

Geniatech ARM SOM is engineered to empower product designers by simplifying embedded system development. By leveraging a modular design and a robust AI + software ecosystem, our SOM solutions accelerate product time-to-market while reducing both development risks and overall costs. Whether you’re targeting industrial automation, medical instrumentation, or smart transportation, Geniatech’s ARM SOM offers the cutting-edge technology