Imagine a soldier on patrol in a war-torn district, equipped with augmented reality (AR) goggles paired with a sophisticated visual recognition AI system. The AI has been trained on thousands of hours of surveillance data, allowing it to detect concealed weapons, identify unusual object placements, and even conduct facial recognition in real-time. It significantly enhances the soldier's situational awareness in a high-risk environment, reducing reliance on human-only observation and minimizing risks through early detection.
The Army has been open about its efforts at integrating AI into military applications. These advancements help streamline the Observe, Orient, Decide, and Act (OODA) loop, enabling faster decision-making compared to traditional human capabilities, which are limited by the body. AI-driven systems can process vast amounts of information at nearly instantaneous speeds, providing decision support that can significantly impact actions taken on the battlefield.
Applications of AI on the Battlefield
The integration of AI into military operations encompasses a wide range of applications. While we often hear of generative AI, other critical uses of AI happen every day. These include visual recognition, predictive maintenance, logistics optimization, and automation processes, many that have been in development for years.
AI's ability to learn from extensive datasets enables it to improve continuously, becoming smarter and more efficient over time. An example of this is seen in Tesla's self-driving technology. Early iterations utilized computing systems with processing power similar to vintage Nintendo consoles; yet, through clever optimization, they achieved remarkable advancements in AI.
Managing Resource Limitations of AI
A challenge confronting AI in military applications is the constraint of resources. Factors such as power consumption, communication capabilities, and processing power must be managed to maximize the effectiveness of AI solutions.
For instance, military applications can capitalize on advanced processing by integrating small system-on-chip (SoC) designs that consume minimal power while adhering to strict energy constraints. Solutions must be developed for edge use cases, rather than relying on generic data center systems.
The Role of Liquid Cooling in AI Systems
As AI systems in military operations become more complex, effective thermal management becomes more important. Past a certain point, conduction and even forced air convection-based cooling solutions become impractical; this is where liquid cooling technology comes into play. By employing thermal management techniques defined within the Sensor Open Systems Architecture (SOSA®), liquid cooling addresses the pressing demands for high-speed processing.
This method enables GPUs, CPUs, and FPGAs to maximize their potential, using efficient cooling systems that range from traditional radiators to advanced solutions such as phase change materials and oscillating heat pipes. Integrating these technologies into military vehicles ensures efficient thermal management at high power density levels or in environments like high elevations, where forced air cooling is less effective.
Overcoming Technological Barriers of AI Deployment
Despite the progress the deployment of AI in the military is making, there are still some hurdles. Beyond physical challenges like thermal management, edge-based AI systems consolidate valuable data, making access and interconnection a challenge while at the same time creating an attractive target for cyber adversaries. In other words, to make use of these powerful systems, they must be connected to wide-area networks while employing network design and cybersecurity best practices.
One approach Curtiss-Wright is taking is to collaborate with Cisco to enable the DoD to field switch ecosystems at the edge in contested cyberspace environments. Now, you aren’t just looking at how fast you can process the data, but also whether something is going to prevent the data from getting in or out of a system at large. It’s about having innovative, adaptable, resilient architectures in edge systems.
Did You Know?
Curtiss-Wright’s VPX3-623 is a Cisco-powered switch that provides high-speed and secure capabilities needed to deploy real-time, high-bandwidth, mission-critical networking in harsh environments. Learn more.
Innovative approaches must be continually developed to overcome these technological barriers, ensuring that military forces can fully leverage the power of AI on the battlefield.
Conclusion
The integration of AI in the battlefield highlights how effective evolving technology can be in protecting soldiers. From visual recognition to predictive maintenance to enhanced situational awareness, adding AI provides military personnel with the real-time data they need to make critical decisions.
Learn more about how Curtiss-Wright solutions support AI in the battlefield here
FAQs
- What is the role of AI on the battlefield?
AI enhances situational awareness, supports predictive maintenance, logistics, visual recognition, and many automations. - What challenges does AI face in the battlefield?
AI faces resource limitations on the battlefield, including power consumption, processing power, and communications capabilities. - How does liquid cooling technology affect the performance of AI systems?
Liquid cooling helps maintain optimal operating temperatures of high-performance components like GPUs, CPUs, and FPGAs, allowing them to operate in demanding environments. - What are potential barriers to deploying AI in the battlefield?
Thermal management is a concern for deploying AI in the battlefield as well as high-speed processing.
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Dominic Perez
Chief Technical Officer
Dominic Perez joined Curtiss-Wright in 2008 as part of the company’s Quality Department. He took an active role in product development and was part of the team that created the first small form factor (SFF) PacStar 1200-Series modules which evolved into the industry-leading PacStar 400-Series product line. In 2013, he led the Quality Systems and Networking Engineering departments, which among other responsibilities created the PacStar Secure Wireless Command Post (SWCP). He was promoted to Vice President of Systems Engineering in 2020 and tasked to lead the development of the company’s next generation of integrated solutions. In 2021, he was promoted to Chief Technical Officer and appointed a Technical Fellow at Curtiss-Wright.
