Complex solutions and the need to monitor them Edge Computing provides the opportunity to move highly complex, multifunctional solutions to the edge in support of the DoD advance compute initiatives. The connected battlefield requires solutions that are not only able to perform but are also capable of meeting the varied environments of the modern-day field of deployment. This daunting task challenges the best system designers to build a solution that is measured in the extreme.
Overlooking thermal design in today’s truck-mounted advanced driver assistance systems can cripple performance, cause critical safety problems, and expose vehicle operators and vendors to liability. Technical leads for autonomous trucks should carefully review ADAS vendors’ thermal design to ensure it meets their performance, durability, and safety requirements. The self-contained ADAS generates enormous heat due to its advanced graphics processing units, which enable the real-time AI inferencing that’s essential for autonomous driving. The heat from these compute units is so high that it strains conventional air-cooling. Liquid cooling is fast becoming a requirement in this emerging automotive market.
Low latency, high-performance solutions are fielded by One Stop Systems (OSS) using the best commercial technologies available. As the mobility of the military, in an ever-increasingly complex battlefield, emerges the need to bring advanced systems to the Edge is clear. The spectacle of advanced weapons stalling a superior fighting force, as evident in Ukraine, is driving the need to build a cohesive, autonomous, and semi-autonomous strategy for the United States. Moving commercial solutions and hardware to the edge has become imperative. This shift for tactical and combat vehicles is based on a compendium of communications and mixed signals to create an asymmetrical Artificial Intelligence (AI) solution that outstrips the enemy's ability to react.
When most of us think about moving data, we don’t think of an amount of data that requires a semi-truck to move it. But as applications at the edge are generating copious amounts of data, the calculus for this bottleneck has led One Stop Systems to build a family of storage solutions to address these needs with an eye for forward, harsh environments.
Bridging the gap between sensors and high-performance compute power is a growing challenge, especially in systems where quick, complex decisions are vital.
The concept of “the edge” in embedded systems has taken on new urgency in the federal space. The need to address the burgeoning amount of high-speed, vital data with powerful AI processing for immediate complex decision and reaction could be likened to the idea of stuffing a data center under the seat of a helicopter (if only!). “The edge” can be defined as “where it’s happening” and in government systems, that’s the field. Traditionally, the problem of linking high-speed sensors and actuators with super-powerful AI resources has been addressed with high-speed data communications. But that has serious limitations in terms of field operations where package size, speed, mobility, and reliability are paramount.
Program managers face hard tradeoffs bringing artificial intelligence to in-the-field use cases. A new AI server from One Stop Systems shows what capabilities they should look for in portable, rugged AI deployments.
Nearly all the current generation of AI compute platforms suffer from failing to integrate and optimize high-performance computing with compact, rugged form factors. The result is that program managers too often end up trading performance for rugged design, or vice versa. One Stop Systems (OSS) has created a new supercomputer-class server that eliminates these trade-offs.
The use of Visual Analytics is advancing rapidly as Artificial Intelligence is employed to build sophisticated tools and processes to review large data sets. By managing these large data sets from an array of sensors and video sources, the OSS solution can decipher threats in real-time with little or no human interaction. The data filtered by an AI engine is presented to aid the user in identifying patterns and actionable intelligence with increased accuracy and insights. The combination of employing AI and Visual Analytics into an actionable solution is at the core of solutions provided by OSS.
The growing list of applications for artificial intelligence (AI) covers a wide area including such things as face recognition, image processing, natural language processing, military decision making, robotic control, natural language translation, data mining - the list goes on.
Program managers are struggling to quickly deploy AI capabilities for real-time applications in the field – outside of data centers. These leaders can speed time to market by utilizing scalable commercial compute solutions from vendors with expertise in real-time, AI-ready platforms built for rugged environments.
The mere mention of artificial intelligence, AI, brings forth images of large, dimly-lit rows of high performance power hungry equipment racks in a data center. At the same time, "the edge" evokes the idea of constrained embedded systems operating in harsh forward locations enduring all types of environmental conditions. All the while taking in data, processing it, and reacting in real-time. As this "edge" becomes ever more demanding and complex, it increases the need for AI levels of performance like that in the data centers but now to be available and useable on the spot and in systems that are transportable at the edge—a big challenge that has been addressed by One Stop Systems.
The growing number of transportable AI applications, from autonomous driving to object identification in military aircraft, increases the need for users and data scientists to focus on overall AI system architecture design rather than individual innovations in a single hardware component or piece of software.
John Palmer, Content Specialist at PureB2B, explains how the time is now to embrace high-performance computing technologies and advanced artificial intelligence capabilities for your upcoming edge computing deployments.