www.design-reuse-embedded.com
Find Top SoC Solutions
for AI, Automotive, IoT, Security, Audio & Video...

'Electronic Nose' Inspired by Neuromorphic Technology

Detecting chemicals via their smells is useful for security and medical applications.

eetasia.com, Mar. 23, 2020 – 

Researchers from Intel and Cornell University have developed an "electronic nose" system that can detect 10 different chemicals as accurately as a state-of-the art deep learning system, but with very little training required. The experiment shows that electronic nose systems could take advantage of neuromorphic computing's easy/quick training ('self-learning') and low power operation, and allows some interesting insight into one potential use case of neuromorphic technology.

Intel researchers, working with olfactory neurophysiologists from Cornell, built a system that uses Intel's Loihi neuromorphic chip to process the data from an array of chemical sensors. A neuroscience-derived algorithm developed by the team predicts whether chemicals such as ammonia, acetone and methane – chemicals which are associated with precursors to explosives, narcotics and certain polymers – are present. The test setup was able to "smell" these chemicals accurately, even in the presence of many other scents.

The test system was trained on a single sample of each smell, and each new target smell didn't affect the ability to detect smells the system previously learned. Compared to a state-of-the-art deep learning system, which required 3,000 samples of training data to reach the same prediction accuracy, training the neuromorphic system was much quicker and made use of the low-power nature of the hardware.

Click here to read more...

 Back

Partner with us

List your Products

Suppliers, list and add your products for free.

More about D&R Privacy Policy

© 2024 Design And Reuse

All Rights Reserved.

No portion of this site may be copied, retransmitted, reposted, duplicated or otherwise used without the express written permission of Design And Reuse.