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Intel neuromorphic nose

27.03.2020

Intel researchers, together with scientists from Cornell University, have created a kind of "electronic nose" that can remember the specific smell of a compound and chemical at a time and identify this smell with high accuracy even if it is masked by other strong smells. The "electronic nose" system is based on Intel's Loihi neuromorphic processor, and its sensitive element is a matrix of 72 chemical sensors.

The Loihi processor is programmed in such a way that its circuits mimic the neurons of the so-called olfactory bulb, the part of the brain that is responsible for smell recognition, as closely as possible. Further development of this system will make it possible in the future to create devices that can detect dangerous chemicals, hidden drugs or explosives, and, of course, make accurate medical diagnoses.

The architecture of the Loihi processor is built to closely match the work of neural circuits and more complex parts of the brain than can be achieved with conventional central processing units, even if equipped with special neuromorphic accelerators. Thanks to this, such neuromorphic processors are able to perform work that is beyond the power of traditional artificial intelligence systems. In addition, the amount of energy consumed by systems with neuromorphic processors is significantly less than the amount consumed by conventional systems that perform comparable tasks in the field of deep machine learning and self-learning.

One of the things that can be implemented on a neuromorphic processor, but is very difficult to do using traditional technologies, is the so-called one-shot learning. The human brain does this with ease, once you feel some kind of characteristic smell, the next time you will certainly recognize it. Artificial intelligence systems using neural networks require training on a huge number of examples, and the worst thing is that a previously trained neural network cannot be trained in a new category without damaging its memory of the previously learned category. To achieve the maximum result, the neural network must be completely retrained using data from all the necessary categories at once.

The next steps that the Intel and Cornell University researchers intend to take will be to make it possible to group related data (smells) into general categories. For example, the system is already able to recognize that the smell corresponds to the smell of strawberries grown in Europe or grown in California, but later it will have to indicate that both smells are the smell of strawberries.

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