Events & News

As AI continues to dominate tech headlines, everyone’s talking about “large models,” “AI acceleration,” and “intelligent computing.” But when it comes to real-world deployment, compute power is the true currency. Have you noticed that more AI companies are no longer relying solely on GPUs—or even TPUs? Instead, a new term is gaining traction: Artificial Intelligence

Artificial Intelligence (AI) models are computational tools designed to simulate aspects of human intelligence. Once trained on large volumes of data, these algorithms evolve into models capable of recognizing patterns, making decisions, and generating insights. The more data they learn from, the more accurate they become. From machine learning and deep learning to generative AI

As artificial intelligence continues to move closer to the edge, choosing the right AI accelerator hardware has become a mission-critical decision for engineers, product designers, and system integrators. Whether you’re building smart cameras, autonomous machines, or retail analytics devices, your hardware must deliver high performance, low latency, and power efficiency—all within constrained embedded environments. This

As edge AI continues to transform industries—from smart cities and autonomous vehicles to factory automation and remote monitoring—hardware architecture plays a crucial role in determining success. Developers are constantly faced with a key decision: should they build around a System on Module or a System on Chip? While both approaches deliver powerful compute capabilities, they

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