CEA-Leti Barn-Owl Inspired, Object-Localization System Uses Up to ’5 Orders of Magnitude’ Less Energy than Existing Technology

GRENOBLE, France - July 7, 2022 - Inspired by the barn owl's neuroanatomy, CEA-Leti has developed an event-driven, object-localization system that couples state-of-the-art piezoelectric, ultrasound transducer sensors to a neuromorphic, resistive memories-based computational map. Presented in a paper published recently in Nature Communications , the research team describes development of an auditory-processing system that increases energy efficiency by up to five orders of magnitude compared to conventional localization systems. "Real-world sensory-processing applications require compact, low-latency, and low-power computing systems," the paper, "Neuromorphic Object Localization Using Resistive Memories and Ultrasonic Transducers", explains. "Enabled by their in-memory, event-driven computing abilities, hybrid memristive-complementary metal-oxide semiconductor (CMOS) neuromorphic architectures provide an ideal hardware substrate for such tasks." Neurobiology offers a spectrum of ultralow-power solutions to efficiently process sensory information, as different animals and insects have evolved to effectively perform difficult tasks with limited power. At the heart of biological signal processing are two fundamental concepts: event-driven sensing and analog in-memory computing. -"We drew inspiration from biology to incorporate these two aspects of computation into our hardware, leveraging CEA-Leti's state-of-the-art ultrasound sensors and resistive memory technologies," said Elisa Vianello, senior scientist and Edge AI program coordinator, and senior author of the paper.