Last week, Texas Instruments (TI) introduced two new series of real-time microcontrollers that combine edge AI capability and real-time control intelligent and secure processing in automotive and industrial applications.
In the company's announcement, TI's senior VP for embedded processing, Amichai Ron, said, "Engineers increasingly strive to design automotive and industrial applications that are more energy efficient and can make faster decisions, driving a need for more scalable processing power, expanded memory, and on-chip safety and security enablers that deliver advancements to help engineers achieve more. Today's introduction of enhanced real-time performance and edge AI in our C2000 portfolio will empower engineers to solve more complex problems and reach higher levels of system efficiency, safety and sustainability."
Since everyone talks about edge AI today, we caught up with Amichai Ron at electronica last week in Munich, Germany, to dig a little deeper. In the interview, he talks about some of the current trends for edge AI in various industries.
He highlights how edge AI is becoming increasingly prevalent, with customers across a wide range of industries exploring its potential. The ability to process data locally using AI algorithms offers numerous advantages, including reduced latency, enhanced privacy and increased autonomy.
He adds that a significant trend is the integration of edge AI into real-time control systems. This is where he talks about TI's new F28 P-55 chip, as an example, that combines real-time control capabilities with an AI accelerator, allowing for sophisticated data analysis and decision-making at the edge. He said this integration is particularly valuable in applications like solar inverters and motor control, where rapid response times and precise control are essential.
He also talks about the increasing adoption of automation, particularly in areas like robotics and manufacturing, driving the need for advanced edge AI capabilities. Robots and automated systems rely on edge AI for tasks like sensor data processing, object recognition and navigation. As automation expands into new industries, including the food industry and TI's own factories, the demand for edge AI solutions will continue to grow, he said. We also discuss what metrics are used to determine the performance of edge AI systems.