Ongoing innovation in semiconductor technologies, algorithms and data science are making it possible to incorporate some degree of AI inferencing capability in an increasing number of edge devices.

The need for fast, efficient and scalable data processing has never been greater. The dramatic growth we’ve seen in computing power, 5G networks, IoT devices and other data-intensive applications will ...

Performance enhancement, cost reduction, data security, and improved energy efficiency are the end goals for optimizing AI workloads at the edge.

With the generative AI boom in full swing, the supply chain is shifting its focus to developing edge AI device applications. In 2024, AI PCs and smartphones will undergo market sales tests, and ...

In popular media, “AI” usually means large language models running in expensive, power-hungry data centers. For many applications, though, smaller models running on local hardware are a much better ...

ANAFLASH, a Silicon Valley-based pioneer in low power edge computing, has acquired Legato Logic's time-based compute-in-memory technologies and its industry veterans. This strategic acquisition ...

In recent decades, computer scientists have been developing increasingly advanced machine learning techniques that can learn to predict specific patterns or effectively complete tasks by analyzing ...

The combination of edge computing and 5G networks is enabling utilities to manage data more effectively and shrink decision-making and response times, while also reducing the burden on centralized ...

Large language models (LLMs) such as GPT-4o and other modern state-of-the-art generative models like Anthropic’s Claude, Google's PaLM and Meta's Llama have been dominating the AI field recently.

As a subset of distributed computing, edge computing isn’t new, but it exposes an opportunity to distribute latency-sensitive application resources more optimally. Every single tech development these ...