Building tomorrow's innovations with today's edge AI-enabled devices

Edge artificial intelligence (AI) is no longer a distant concept—it is already reshaping how modern electronics operate. By processing data locally on a device rather than relying on the cloud, edge AI is powering the systems that keep our world running.

“Cloud-based AI comes with inherent limitations,” said Patrick Zeng, general manager for Building Automation at Texas Instruments (TI). “When a central processor handles simple functions such as proximity or keyword detection, the result can be unnecessary latency and higher power consumption. In these cases, edge AI delivers significant advantages.”

With edge-AI capabilities now built across TI’s portfolio of embedded solutions, engineers have more room than ever to innovate. To better understand how this technology is transforming real-world design, TI’s AI experts explain how edge intelligence is unlocking new performance levels in healthcare, renewable energy and building automation.

Making Healthcare More Accessible and Efficient

Edge AI is accelerating the shift from traditional hospital-centric care to a faster, more distributed, patient-focused model.

“Everyday devices are beginning to provide insights that previously required a clinical visit,” said John Varela Munoz, general manager for Systems and Engineering Marketing at TI.

For example, a wearable heart monitor can analyze rhythm data locally, reduce noise, and flag irregularities in real time. Running AI models directly on the device allows it to learn an individual’s baseline trends and tailor alerts accordingly, giving users more reliable and personalized results.

Today’s edge-AI models typically enhance accuracy using one or two sensor inputs. But in the next five to ten years, models will be able to merge data from multiple sensors—including temperature, pressure, and electrical signals—to form a richer picture of a patient’s condition.

“AI will be able to personalize the experience, shorten learning cycles, and adapt algorithms based on each individual,” John explained.
For instance, while a 90% probability match may lead to a correct diagnosis for most patients, edge AI can capture subtle factors from the remaining 10%—adjusting the decision-making process using detailed patient history and behavioral patterns.

Accelerating Adoption and Innovation in Sustainable Energy

As renewable energy becomes more deeply integrated into everyday life, edge AI can further improve reliability and performance. Analysts expect renewables to become the world’s largest energy source by 2030. TI is developing edge-AI technologies that strengthen these systems and enhance their robustness.

TI’s pre-trained edge-AI arc-fault detection model for solar panels, for example, has boosted detection accuracy by nearly 20%. By reducing false triggers and unnecessary downtime, edge-AI-enabled detection saves users significant time and service costs.

In solar inverters and energy-storage systems—where advanced power-conversion architectures operate across wide voltage ranges—running neural networks on edge-AI-accelerated MCUs enables more efficient soft-switching and reduces power loss. This leads to higher system efficiency and improved long-term performance.

Edge AI can also optimize electrochemical impedance spectroscopy (EIS) algorithms used in energy-storage systems. This enables more precise estimates of state of charge and state of health, extending battery life and improving safety by predicting thermal-runaway risks more accurately.

“TI was one of the first semiconductor companies to introduce microcontrollers with AI accelerators,” said Henrik Mannesson, general manager for TI Grid Infrastructure. “These accelerators make it possible for customers to deploy AI in applications that were previously out of reach.”
Integrated neural-network processing units (NPUs) help systems achieve over 99% fault-detection accuracy.

Henrik added, “We will continue developing impactful use cases for the energy sector, while also building flexible tools so customers can innovate in ways we can’t yet anticipate.”

Smarter, Safer and More Efficient Buildings

A major challenge in building automation is maintaining the right balance between performance, privacy and energy consumption. “Edge AI gives engineers the ability to achieve that balance directly on the device,” Patrick noted.

Take motion detectors as an example. Traditional passive infrared sensors often misfire due to HVAC airflow or pets moving across a room. By integrating edge AI, detectors can distinguish between people, background noise and other moving objects—leading to more accurate lighting, security and comfort controls.

The same principle applies to other applications as well. Using audio-event detection, high-performance embedded processors with edge AI can handle voice recognition in devices like security cameras or glass-break sensors, while maintaining low power consumption and high accuracy. HVAC systems can also learn occupancy patterns and interpret temperature and humidity signals, making automatic adjustments that improve comfort and reduce energy use.

All of these applications share one major benefit: prediction.
“When engineers use AI, the key question becomes: what should the device be able to predict?” Patrick said.

The Future: Boundless Possibilities

Healthcare, building automation and renewable energy are only the beginning.

“As we continue working closely with designers, we’re laying the foundation for effortless adoption of edge AI and the breakthroughs that will follow,” John said. “Edge AI is already making devices smarter, more intuitive and more efficient—so what comes next, and where will it take us?”