M. Imani
AI Sensation, California, United States
Keywords: Intelligent Sensing, Machine Learning, Brain-Inspired Computing, Energy Efficiency
Defense applications often analyze collected sensor data using machine learning algorithms. Unfortunately, the existing sensing systems lack intelligence about the target and naively generate large-scale data, making communication and AI computation significantly costly. However, in many cases, the data generated by sensors only contain useful information for a small portion of the sensor activity. For example, AI algorithms continuously process the visual sensors used for environmental/security monitoring to detect sensitive activities. Still, these sensors only carry out useful information for a short time. On the other hand, biological sensors intelligently generate orders of magnitude less amount of data. Our goal is to develop semantic-based AI algorithms that provide real-time feedback to sensors to ensure they only generate data needed for learning purposes. We also develop a novel framework that tightly integrates with a sensing circuit and AI algorithms to dynamically control the sensor functionality in a fully self-supervised manner. Our approach is expected to provide up to four orders of magnitude data reduction from sensors. The results from this research will broadly impact many sensors used in DoD applications, including national security, energy management, infrastructure, and autonomous systems.