Computer server redundancy, including backup power supplies, RAID storage devices and applications that automatically fail-over, keeps critical systems up and running longer than non-redundant systems. Similarly, effective system monitoring can provide early warning of failures and allow system managers to remotely manage these systems, further improving application uptime. While the concepts of computer system redundancy and system management are well-established in all levels of computing, from the personal computer to the largest hyperscale datacenters, the unique challenges of placing datacenter-class computing elements performing AI applications in mobile edge environments, like aircraft, ships, and land vehicles, brings unique challenges to system redundancy and management.
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5G networks are the key for a worldwide digital transformation, impacting AI and edge computing as well as smart cities, while also providing high-speed data transfer in real-time for enterprises across every vertical market segment. New applications are constantly emerging on the market, taking advantage of the cellular networks’ super-low latency and much higher bandwidth.
With the well-documented shortage of semi-truck drivers currently in the United States, where do we stand in terms of developing and deploying autonomous trucks? Will autonomous long-haul trucks take jobs away from drivers? Are fully autonomous trucks safe for the roads? What does the future look like for autonomous trucks? These are all great questions. This blog will discuss all that and more, so let’s jump in and discuss it.
In this video, Jim Ison will do a quick walkthrough of the OSS 4U Pro Gen4 PCIe Expansion System. The 4U Pro is a rugged AI Transportable system that provides optimized, configurable PCIe Gen4 expansion for edge AI applications with up to 256 Gigabytes per second (GB/s) bi-directional host to expansion bandwidth and 768 GB/s aggregate system bandwidth.
Typical Artificial Intelligence workflows are well understood. The classic example is image recognition distinguishing between an image of a cat or a dog. The first step is performed by data scientists who create a model they train using large sets of tagged data. The models are iteratively refined by the data scientists to achieve higher and higher levels of accuracy. Once the refined model is trained it can be deployed and new images can be presented to it for inferencing. The AI inferencing process results in a never seen before image being classified as either a cat or a dog.
When you take a step back and look at today’s high-performance computing and AI technological ecosystem, it is a field dominated by the power of GPUs in compute acceleration. For the layperson, the term “Graphical Processing Unit” would not stand out as the core component in processing massive datasets at unprecedented speeds – with no graphics involved. The transition from CPU to GPU core computing in HPC and AI/ML applications, and the further development of GPU technologies since, is the fundamental driving force behind today’s most cutting-edge applications. Modern AI spans practically all industries including piloting the most sophisticated autonomous vehicles, detecting previously unseeable fraud patterns in the financial industry, and increasing productivity through automation in mining, oil and gas, and farming.
Independent of the administration in office, one of the top priorities for today and the next couple of decades is the deployment of Artificial Intelligence (AI) throughout all the branches of the armed forces. Many of these applications are AI Transportables requiring performance without compromise in harsh and challenging environments. This post will cover some of the driving factors, paint the picture of tomorrow and discuss how we will continue to protect our freedom with military superiority.
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