The world today, dominated by data-centric applications, require local data processing or computing at the edge, known as edge intelligence, to decide on a course of action autonomously, without communicating data to cloud platforms. So far, Moore’s Law scaling of semiconductor devices provided the needed hardware support for the heavy workload of AI computing. However, with the end of Moore’s law scaling and limitations of von Neumann computing architectures, the performance and energy efficiency of conventional hardware for AI computations are facing a severe roadblock. In the first part of my talk, I will discuss the promising new discoveries of advanced CMOS-compatible HfO₂-based ferroelectric devices and the electronics based on ferroelectric building blocks integrated on advanced CMOS technology nodes that can provide the needed support to the AI computing tasks, for instance in in-memory computing in data-flow architectures that can enable more than 1000X energy-efficient AI accelerators needed for edge intelligence. Smart edge intelligent IoT devices can enable new applications, for example, autonomous vehicles, robotics, industrial automation, 24x7 health monitoring, space and quantum technologies, that demand higher performance to support local embedded intelligence, real-time learning, and decision making. The field has the potential to drive the next phase of growth in the semiconductor industry.

In the second part of my talk, I will discuss an example application of brain-inspired neuromorphic hardware in autonomous cars. Autonomous vehicles rely heavily on accurate multi-sensor fusion to perceive their environment and make driving decisions. However, conventional AI-based perception systems face challenges in irregular conditions such as poor visibility, obstructions, or adverse weather conditions which can lead to incomplete information to the central computing and navigation system severely impacting the perception accuracy, threatening vehicle and pedestrian safety. In a recent work we have developed a memristor-based associative learning circuit that enhances fault tolerance by dynamically adapting to multi-sensor inputs, including camera, LiDAR, radar, and ultrasonic sensors. The proposed circuit dynamically reinforces patterns, allowing the system to retain decision-making capabilities even when certain sensors fail or provide incomplete data. The system demonstrates an average error of 6.98% across ten critical driving scenarios, with a power consumption of ~152 mW per scenario, confirming its robustness, energy-efficiency and adaptability in case of sensor failures and under-performance. I will discuss how this kind of learning circuit can act as a scalable, neuromorphic alternative for real-time adaptive decision-making in autonomous vehicles, bridging the gap between traditional digital computation and biologically inspired learning mechanisms.