The CES consumer electronics show in Las Vegas, which has become the show to highlight the latest and greatest in consumer technology, is about to get in full swing. Nvidia Corp. has been a mainstay at the show for years, first through its gaming business, but over the years it has broadened its presence into areas such as autonomous vehicles and artificial intelligence.
Artificial intelligence promises to revolutionize almost all industries, but one of the use cases where it can have a significant impact is the slow and error-prone processing of medical images.
Yet AI in radiology hasn’t been broadly adopted because of the complexity of clinical workflows and the lack of industry standards. In fact, according to Nvidia Corp., 99% of medical imaging AI applications never make it to patients because they can’t be deployed at large scale.
The infusion of artificial intelligence (AI) into everything is literally changing our lives on a day-to-day basis. People use speech interfaces, purchase items based on recommendations, and sign into phones with facial recognition routinely because it makes their lives easier.
Nvidia Inc. is positioning its open DOCA software framework as the fastest and easiest way to speed up the development and performance of data processing unit applications.
DOCA is a software development kit for Nvidia BlueField DPUs (pictured). For those not familiar with BlueField, it provides data center infrastructure-on-a-chip, optimized for high-performance enterprise and cloud computing. Because DOCA is tied to BlueField, there’s a misconception that it’s closed and proprietary.
Since the birth of computing, networks have evolved alongside compute architecture. From mainframes to client servers to branch office computing, networks have had to keep pace by undergoing their own evolutionary processes. Today, compute has become highly distributed and that’s driving yet another major shift: to remain effective, networks now must be distributed.