Ultra-Low Latency Edge AI for Real-Time Control Systems

Explore a 13-minute conference talk that demonstrates how ultra-low-latency edge AI transforms real-time control systems by predicting error trajectories and intervening within control loops in approximately 100 microseconds. Learn how this technology addresses the limitations of traditional PID controllers in nonlinear systems, which often suffer from overshoot, oscillation, and performance tradeoffs that cannot be solved simply by adding more processing power or sensors. Discover how Ultra-Edge technology extends PID behavior into nonlinear territory while operating on modest 32 MHz platforms, drawing parallels to the smartphone industry's dynamic voltage and frequency scaling revolution. Examine practical applications across motor drives, inverters, and energy storage systems, where the technology works directly from existing operational data to chart efficient paths and act locally, sending only meaningful transients upstream for fleet analytics and predictive maintenance. Understand the broader implications for industrial automation and power grid stability, including how torque transitions in motor drives become smoother with fewer current spikes, and how grid-forming converters coordinate with renewables and battery energy storage to deliver synthetic inertia. See how microsecond-level responses enable converters to provide stabilizing support during disturbances, facilitating higher renewable energy penetration and supporting net-zero goals. Gain insights into the technical approach that filters millions of samples per second down to high-value events, reducing noise while preserving critical signals for control teams. The presentation includes clear examples, measured results, and a comprehensive roadmap for adoption, covering everything from pilot programs and soft IP implementations to demo platforms and expanding model libraries for real-time control applications in nonlinear systems.