Neuromorphic Device with Low Power Consumption

By Maurizio Di Paolo Emilio, EETimes (August 1, 2022)

Compact, low–latency, and low–power computer systems are required for real–world sensory–processing applications. Hybrid memristive CMOS neuromorphic architectures, with their in–memory event–driven computing capabilities, present an appropriate hardware substrate for such tasks.

To demonstrate the full potential of such systems and drawing inspiration from 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 with a neuromorphic computational map based on resistive random–access memory (RRAM).

CEA–Leti built and tested this object tracking system with the help of researchers from CEA–List, the University of Zurich, the University of Tours, and the University of Udine.

The researchers conducted measurements findings from a system built out of RRAM–based coincidence detectors, delay–line circuits, and a fully customized ultrasonic sensor. This experimental data has been used to calibrate the system–level models. These simulations have then been used to determine the object localization model’s angular resolution and energy efficiency. Presented in a paper published recently in Nature Communications, the research team describes the development of an auditory–processing system that increases energy efficiency by up to five orders of magnitude compared with conventional localization systems based on microcontrollers.

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