WASHINGTON — The U.S. Army still plans to release of its request for proposals in December to replace the Bradley Infantry Fighting Vehicle, and it wants industry to prioritize an open architecture in its designs.
“The network is almost more important in some ways than building the combat vehicles,” Maj. Gen. Brian Cummings, program executive officer of ground combat systems, told Defense News in an interview ahead of the Association of the U.S. Army’s virtual conference.
The future optionally manned fighting vehicle will need the flexibility to be networked with other capabilities across the battlefield, and designed such that capabilities can plug into the vehicle at the forward edge. This realization was highlighted during the Army’s Project Convergence exercise at Yuma Proving Ground, Arizona, which wrapped up last month and during which an OMFV surrogate played a part.
The Army will focus on the effort to develop OMFV with an
A camera or a computer: How the architecture of new home security vision systems affects choice of memory technology
A long-forecast surge in the number of products based on artificial intelligence (AI) and machine learning (ML) technologies is beginning to reach mainstream consumer markets.
It is true that research and development teams have found that, in some applications such as autonomous driving, the innate skill and judgement of a human is difficult, or perhaps even impossible, for a machine to learn. But while in some areas the hype around AI has run ahead of the reality, with less fanfare a number of real products based on ML capabilities are beginning to gain widespread interest from consumers. For instance, intelligent vision-based security and home monitoring systems have great potential: analyst firm Strategy Analytics forecasts growth in the home security camera market of more than 50% in the years between 2019 and
In an interview published on the eve of Apple’s “iPhone 12” launch event, Apple VP of platform architecture Tim Millet has explained some of the work that went into the A14 Bionic system-on-chip, and what it means for the future of Apple’s chip designs.
Revealed in the iPad Air launch during Apple’s first special event, the A14 is widely anticipated to make an appearance during Tuesday’s “Hi, Speed” event, where Apple is expected to unveil its 2020 iPhone lineup. Millet offered more details about the A14’s design and creation.
Made using a 5-nanometer process, the A14 packs in 11.8 billion transistors onto the chip, up from the 8.5 billion of the A13, with the changes enabling Apple to be more precise in how it uses the chip to shape the user’s experience.
“One of the ways chip architects think about features is not necessarily directly mapping [transistors]
Just after coming off two major industry announcements – the introduction of the highest performing consumer graphic cards and the proposed acquisition of Arm – Nvidia launched its largest virtual Graphics Technology Conference (GTC) on October 5, 2020. Unlike the spring 2020 GTC, which was changed to a virtual conference at the last moment due to COVID-19, this one resembled a more traditional GTC with the kickoff by CEO Jenson Huang revealing a flurry of product and technology announcements. Dressed in his trademark leather jacket and standing in the middle of his kitchen (again), Mr. Huang provided a glimpse into Nvidia’s new solutions for the data center, edge AI, healthcare, and a new suite of collaborative tools.
There were two key areas of focus for this GTC – the growing pervasiveness of AI and a proposed change
VMware and Nvidia are integrating the latter’s artificial intelligence applications for unified management of apps, security and data processing unit accelerators.
The partnership secures Nvidia’s role in hybrid clouds as VMware outlined an architecture that incorporates data processing units (DPUs) in the data center, cloud and edge. Specifically, AI software on Nvidia’s NGC hub will be integrated into VMware vSphere, Cloud Foundation and Tanzu.
Both companies said the bet was that the integration would be able to speed up AI adoption in enterprises.
Krish Prasad, general manager of VMware’s cloud platform business, said “AI workloads no longer need any kind of specialized set up based on bare metal or specialized tools to run it.”
VMware and Nvidia also said they will partner on Project Monterey to create an architecture based on SmartNIC technology and include Nvidia’s programmable BlueField-2 DPU to support machine learning and data-focused apps.