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Object-Localization System Uses Less Energy than Existing Technology

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

An event-driven, object-localization system that couples state-of-the-art piezoelectric, ultrasound transducer sensors to a neuromorphic, resistive memories-based computational map has been introduced by CEA-Leti.

CEA-LetiPresented 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.

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.

CEA-Leti built and tested this object localization system with the help of CEA-List, University of Zurich, University of Tours and University of Udine researchers. The team leveraged CEA-Leti’s successes in developing piezoelectric micro machined ultrasound transducer (pMUT) sensors and its advancements in spiking neural networks based on resistive memory technologies.

The researchers’ first challenge was developing a pre-processing pipeline that extracts the key information from pMUTs, which encode information based on brief events or spikes. This temporal signal coding leads to higher energy-efficiencies compared to traditional continuous analogue or digital data, so that only relevant data are processed.

“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.”

 “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. “In particular, we focused on the acoustic-based, object-localization task. Owls efficiently solve this problem and thus we extrapolated their computational principles into our system.”

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Nitisha Dubey

I am a Journalist with a post graduate degree in Journalism & Mass Communication. I love reading non-fiction books, exploring different destinations and varieties of cuisines. Biographies and historical movies are few favourites.

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