The Internet-of-Things (IoT)—the network of physical objects embedded with sensors, software and other technologies to link and exchange data over the internet—brings us a richly connected set of ultra-low-power processors that can sense and collect data remotely. However, the data they collect must be computed securely and privately. That’s why we need more diverse and unconventional processors that can keep pace with the growing need for better computational capacity.
Dr. Natalie Enright Jerger, Canada Research Chair in Computer Architecture, is exploring machine learning as a promising approach to gathering insight from such large volumes of data. She and her research team are working on ground-breaking processor innovations that will improve the performance, energy efficiency and security of battery-less IoT devices and machine learning applications.