Many jobs in the air, on the ground, underwater, or on the surface call for smart unmanned vehicles, giving warfighters ways to project capability without risk to human lives. These vehicles are also taking on more complex missions requiring more intelligence.
Sensor resolution continues to grow. More pixels in a scene mean more pixels must be processed to find objects. However, the available window of time to find those objects is usually short. Sending images over a wireless network for processing increases delays and steals network bandwidth needed for other communication.
Improved edge AI processing helps vehicles identify threats faster and make basic decisions about next steps in their mission. Efficient processing in an ASIC lowers power consumption, improving range and loiter time.
AI inference can also augment human operators gathering and processing intelligence in shipboard, airborne, and ground shelter-based applications. Using AI to sift through volumes of incoming data, highlighting areas that need further attention, decreases operator stress and increases productivity.
AI can recognize patterns in one dimensional (such as SIGINT) or two dimensional (such as video reconnaissance imagery) streams of data efficiently. As sample or frame rates increase, determinism and latency are critical parameters in analysis.
With threats constantly evolving, reconfigurable AI inference platforms are a necessity. Hardware-optimized solutions running specific neural network models lose flexibility over a program life cycle. FPGA-based solutions offer high-performance, reconfigurable inference without the SWaP and programming burden of GPU-based solutions.
Much of modern warfare now occurs in cyberspace. Systems are updated often, and threats are keeping pace, morphing to stay ahead of defenses. The higher value information has, the more intensely attackers try to expose or corrupt it.
Cybersecurity is using AI inference to help spot probing patterns before full-scale attacks develop. Once an attack begins, AI can guide system and network reconfiguration to mitigate damage faster than human personnel can respond. AI also helps sort through volumes of data for post-incident analysis, highlighting areas for experts to scrutinize.
Ultimately, no system is inherently secure, no matter how carefully designed or maintained. Security comes down to faster threat detection and response. AI inference will be part of every modern cybersecurity system because it offers capability to detect even obscure patterns that would otherwise escape real-time human observation.
Given the tectonic shift in information processing at the edge, companies are now seeking near cloud level performance where data curation and AI driven decision making can happen together. Due to this shift, the market opportunity for the EdgeCortix solutions set is massive, driven by the practical business need across multiple sectors which require both low power and cost-efficient intelligent solutions. Given the exponential global growth in both data and devices, I am eager to support EdgeCortix in their endeavor to transform the edge AI market with an industry-leading IP portfolio that can deliver performance with orders of magnitude better energy efficiency and a lower total cost of ownership than existing solutions."
