At the opening of the Intel Developer Forum in San Francisco, Paul S. Otellini unveiled Intel's next-generation, power-optimized micro-architecture for future digital home, enterprise, mobile and emerging market platforms -- and low-power products that will enable a new category of converged consumer devices. Intel will introduce a micro-architecture in the second half of 2006, that combines the company's current Intel NetBurst and Pentium M micro-architectures and adds new features. "We will deliver factor of 10 breakthroughs to a variety of platforms that can reduce energy consumption tenfold or bring 10 times the performance of today's products. At the same time, Intel innovation will continue to deliver unique digital enterprise, home, office and mobile features, such as greater manageability, security and virtualization, along with an increasing capability to manage and view digital content," Otellini said. Intel currently has more than 10 processor projects that contain four (quad-core) or more processor cores per chip. Otellini also announced that forthcoming lower-power products will lead to a new category of ultra energy-efficient Handtop PC devices that provide a converged communication and PC-like experience but require less than a watt of processing power and weigh under a pound.
Enterprise AI adoption keeps running into the same wall: the moment sensitive data, model weights, or agent memory leave a tightly controlled environment, security teams lose visibility into what happens to them while the workload is actually running. Confidential computing exists to close that gap, and new research from ABI Research shows the technology has matured enough, across CPUs, GPUs, containers, and now agentic AI, to become a foundational requirement for enterprises building sovereign AI infrastructure: computing environments they control end to end rather than simply rent. Protection is Moving From CPUs to GPUs ABI Research finds that CPU-based confidential computing is closest to mass-market adoption, the product of years spent hardening chip-level isolation for general workloads. GPU-based confidential computing is now generating the strongest momentum, and for good reason. It addresses one of the largest unresolved gaps in AI security: protecting data and models while inf...