10 HARNESSING AI IN SPACE www.vicorpower.com Issue 1 2026 Power Electronics Europe www.power-mag.com Harnessing AI in space to enable faster communication and a new era of innovation Rad-tolerant DC-DC converter modules power AI1 Transponder for in-orbit computation By Salah Ben Doua, Sr. Principal Field Application Engineer EMEA Aerospace and Defense & Satellite Solutions, Vicor Since 2010 the number of satellites orbiting earth has increased by 25 times. Satellites cost millions of dollars to deploy and are designed to remain in orbit 5 to 10 years, requiring reliable and robust onboard processor systems to support the duration of a mission. The demand for smaller satellites with increasingly sophisticated computational capabilities is pushing the limits of the latest ultra-deep-submicron FPGAs and ASICs and their power delivery networks. These high-performance processors have demanding, low-voltage, high-current power requirements and their system design is further compounded by the complexities of managing thermal and radiation conditions in space. Embracing these challenges, Spacechips has introduced its Spacechips AI1 Transponder product, a small, onboard processor card containing an Adaptive Compute Acceleration Platform (ACAP) AI accelerator. The system delivers up to 133 tera operations per second (TOPS) of performance to support new real-time autonomous computing applications, while ensuring the reliability and longevity to complete longer missions. “Many spacecraft operators simply don’t have sufficient bandwidth in the RF spectrum to download all of the data they’ve acquired for real-time processing,” said Dr. Rajan Bedi, CEO of Spacechips. “An alternative solution is accomplishing the processing in-orbit and simply downlink the intelligent insights.” The impact of in-orbit computing for applications in space and on Earth Spacechips is harnessing powerful artificial intelligence compute engines capable of enabling in-orbit AI to address a variety challenges including monitoring mission critical spacecraft system health. AI algorithms can continuously assess the health of onboard subsystems—power, thermal, altitude control and communications—by learning normal operational patterns and detecting anomalies early. Beyond satellite operations, innovative applications address a variety of space-related and Earth-bound problems that benefit from faster communications that enable proactive responses. 1. Tracking space debris to avoid costly collisions AI-equipped satellites can autonomously detect, classify and track space debris when direct line-of-sight communication with Earth is not possible using real-time data captured from onboard imaging sensors. Traditional ground-based monitoring often struggles with smaller, fast-moving fragments, but AI can process sensor input in real-time to predict trajectories and identify collision risks. Neural networks trained in orbital mechanics help refine debris catalogs and update avoidance maneuvers autonomously. 2. Identifying severe weather patterns AI on satellites observing the Earth’s atmosphere can enhance the identification and prediction of severe weather events. Instead of simply collecting imagery, an onboard neural network can segment cloud types, estimate storm intensity and flag rapidly forming systems for higherpriority downlink. This allows faster response to severe weather events and improves localized forecasting accuracy, especially over oceans and remote regions where ground sensors are sparse. 3. Detecting surface hotspots and predicting flashpoints Infrared sensors paired with AI can detect temperature anomalies such as wildfires, volcanic activity or industrial accidents. Machine learning models trained on historical patterns can distinguish between benign heat sources and emerging “flashpoints,” enabling near-real-time alerts to disaster response agencies. Predictive modeling also helps identify regions at elevated risk before ignition occurs, allowing for preemptive action. 4. Reporting critical crop production rainfall data AI can combine multispectral imaging, GPS-tagged agricultural zones and rainfall data to assess crop health, yield potential and water stress. Models can distinguish between soil moisture variations, nutrient deficiencies and disease indicators. When fused with climate and precipitation inputs, onboard AI can deliver rapid, localized agricultural intelligence to governments and farmers, supporting sustainable food production and resource allocation. Today’s low-Earth-orbit (LEO) observation spacecraft can establish direct line of sight over a specific region only about once every 10 minutes. If satellites were trained to fill those blind spots using AI algorithms, emergency management teams could make faster, better-informed decisions regarding which potential flashpoint areas are the most vulnerable. The goal is to design intelligent, autonomous real-time decision-making when direct line-of-sight communication with Earth is not possible. “In the case of disaster management, whether it’s a wildfire or a devastating flood, the difference between seconds and minutes is huge in terms of protecting people and wildlife, and reducing the destruction to infrastructure and property,” Bedi said. “If we can make these decisions quicker, we can minimize damage and loss of life.”
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