FerroElectronics for Edge Intelligence
A. Keshavarzi, Kai Ni, Wilbert van den Hoek, Suman Datta, Arijit Raychowdhury
Abstract
The future data-centric world demands edge intelligence (EI) - the ability to analyze data locally and to decide on a course of action autonomously. Challenges with Moore's Law scaling and limitations of von Neumann computing architectures are limiting the performance and energy efficiency of conventional electronics. Promising new discoveries of advanced CMOS-compatible HfO2-based ferroelectric devices open the door for FerroElectronics; electronics based on ferroelectric building blocks integrated on advanced CMOS technology nodes. It will enable much needed improvement in computing capabilities making EI a reality. In-memory computing in data-flow architectures is at the core of FerroElectronics. This approach will enable building 1000X more compute-energy-efficient small-system AI engines needed for EI. Smart edge intelligent IoT devices enable new applications, for example, micro Drones(uDrones), that demand higher performance to support local embedded intelligence, real-time learning, and autonomy. They will drive the next phase of growth in the semiconductor industry.