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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“Edge Computing” is a term which has been widely adopted by the tech sector. Dominant leaders in accelerated computing have designated “Edge” as one of their fastest-growing segments, with FY24 revenue projected to be nearly $100 billion. The boom in the market for Edge Computing has become so significant that it is increasingly common to see companies create their own edge-related spinoff terms such as ‘Rugged Edge’, ‘Edge AI’, ‘Extreme Edge’, and a whole slew of other new buzzwords.
The landscape of modern warfare is undergoing a profound transformation with the integration of cutting-edge technologies, and at the forefront of this evolution are autonomous military vehicles. Datalogging, a seemingly inconspicuous yet indispensable technology, plays a pivotal role in shaping the capabilities and effectiveness of these autonomous marvels. In this blog post, we delve into the critical role of datalogging in autonomous military vehicles and its impact on the future of defense strategies.
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.
The realm of scientific simulations is a realm of immense complexity, where models often involve millions of interacting parameters and trillions of calculations. HPC systems provide the computational muscle to tackle these daunting challenges, but they also present unique technical hurdles.
In the dynamic realm of machine learning, staying ahead of the curve requires not only innovative algorithms, but also cutting-edge hardware. ML Commons, a consortium of industry leaders, has emerged as a pivotal player in this space. In this blog post, we'll explore the significance of ML Commons, with a special focus on its compute and inferencing benchmarks tailored for edge scenarios.
Generative, Inferencing and Natural Language Processing (NLP) AI applications have all seen explosive growth (NVIDIA market cap growth 3200%) and rapid proliferation in the last two years across commercial market segments such as Automotive, Aerospace, Manufacturing, Transportation, Banking, and Entertainment. The commercial consumption of AI has quite directly driven the need for High Performance Compute (HPC) solutions, and, specifically HPC solutions that operate in some of the most extreme environments on earth. By comparison, DOD compute systems for air/ground platforms, while significantly ruggedized for austere environments, lack the compute power to employ such technological advancements in AI…or worse, lack the capability to counter AI threats employed by adversaries using commercially available SW/HW.
The rapid onset of AI is managing its ways into every facet of our lives. It is unlikely you can go a day, or even hour, without in some way connecting with, using, or hearing about AI. This is especially true in Edge Processing markets where AI is integrated in vehicles and platforms in the environments we work and play in.
HPC systems are characterized by their high-speed processing power, high-performance networks, and large-memory capacity, generating the capability to perform massive amounts of parallel processing. When a standard desktop with a 3 GHz processor can perform around 3 billion calculations per second, an average HPC system can perform quadrillions of calculations per second. This means HPC systems perform extreme amounts of calculations in seconds, whereas a regular processor would take weeks or even months to complete the same task, thanks to their technology.
In my last blog post, What is the Workhorse Advancing HPC at the Edge?, I highlighted how enterprise applications requiring the highest end compute for their AI workloads at the Edge are leveraging data-center grade NVIDIA GPUs to get even greater performance. Processing and storing data closer to where the action takes place means a decision can be made more quickly, producing reduced latency, improved security, greater reliability, and much higher performance. In this blog, I’ll be covering the transition from using big power-hungry GPUs to smaller form factor electronic control units, better known as ECUs, at the very edge.
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