Cognitive Sensing for Energy-Efficient Edge Intelligence

Edge platforms in autonomous systems integrate multiple sensors to interpret their environment. The high-resolution and high-bandwidth pixel arrays of these sensors improve sensing quality but also generate a vast, and arguably unnecessary, volume of real-time data. This challenge, often referred to as the analog data deluge, hinders the deployment of high-quality sensors in resource-constrained environments. This paper discusses the concept of cognitive sensing, which learns to extract low-dimensional features directly from high-dimensional analog signals, thereby reducing both digitization power and generated data volume. First, we discuss design methods for analog-to-feature extraction (AFE) using mixed-signal compute-in-memory. We then present examples of cognitive sensing, incorporating signal processing or machine learning, for various sensing modalities including vision, Radar, and Infrared. Subsequently, we discuss the reliability challenges in cognitive sensing, taking into account hardware and algorithmic properties of AFE. The paper concludes with discussions on future research directions in this emerging field of cognitive sensors.