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

Implementing artificial intelligence at the edge can not only reduce latency and networking costs but also improve security and unlock the power of distributed intelligence.

As AI workloads move from cloud to edge, the volume of image and sensor data across industries is rising rapidly. Edge ...

Shares of Rigetti Computing (NASDAQ: RGTI) flew higher today, finishing up 15.4%. The jump came as the S&P 500 and Nasdaq ...

This demand represents significant opportunities for economic growth and innovation, but it also introduces significant ...

By Phyllis Migwi DEC 8 - 1995 was in some respects a banner year for the nascent technology industry – the PalmPilot was a smash hit, PlayStation took the Kenya breaking news | Kenya news today | ...

Both cloud-based and edge AI hardware will continue getting better, but the balance may not shift in the NPU’s favor. “The cloud will always have more compute resources versus a mobile device,” said ...

President Trump’s decision to allow Nvidia to sell its chips to China has raised questions about whether he is prioritizing ...

Several of Wall Street's savviest billionaire money managers purchased this stock during the September-ended quarter.

Insights from Rehlko CEO Brian Melka looking at C&I power demand trends within and without AI and data centers.

In eastern China, Hangzhou-based Zhejiang Laboratory has already put up a 12-sat mini computing constellation dubbed ...

The potential data center, called Project Tango, is causing much angst for residents in the western communities of the county ...