Catalog of SIP Cores
System on Chip design resources
System on Chip design resources
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.