Computing Chip

In these times of artificial intelligence, this no longer seems so far fetch for example, when a mobile phone can recognise faces or languages. With more complex applications, however, computers still quickly come up against their own limitations. One of the reasons for this is that a computer traditionally has separate memory and processor units the consequence of which is that all data have to be sent back and forth between the two.

Network of artificial neurons

An international team of researchers from the Universities of Münster (Germany); Oxford and Exeter (both UK) have now succeed in developing a piece of hardware; which could pave the way for creating computers which resemble the human brain. The scientists manage to produce a chip containing a network of artificial neurons that works with light and can imitate the behaviour of neurons and their synapses.

The researchers were able to demonstrate, that such an optical neurosynaptic network is able to “learn” information and use this as a basis for computing and recognizing patterns just as a brain can. As the system functions solely with light and not with traditional electrons, it can process data many times faster.

This integrate photonic system is an experimental milestone,” says Prof. Wolfram Pernice from Münster University and lead partner in the study. The approach could be use later in many different fields for evaluating patterns in large quantities of data, for example in medical diagnoses. The study is publish in the latest issue of the “Nature” journal.

Phase change materials

Most of the existing approaches relating to so called neuromorphic networks are base on electronics; so whereas optical systems in which photons, i.e. light particles, are use are still in their infancy. The principle which the German and British scientists have now present works as follows; so optical wave guides that can transmit light and can be fabricate into optical microchips; hence are integrate with so called phase change materials which are already find on storage media such as re-writable DVDs.

In order to test the extent to which the system is able to recognise patterns, the researchers “fed” it with information in the form of light pulses, using two different algorithms of machine learning. Hence this process, an artificial system “learns” from examples and can, ultimately, generalise them.

In the case of the two algorithms use both in so called supervise; also in unsupervise learning the artificial network was ultimately able, on the basis of given light patterns; hence to recognise a pattern sought one of which was four consecutive letters. A very specific example is that with the aid of such hardware cancer cells could be identify automatically. Further work will need, however, before such applications become reality.

The researchers need to increase the number of artificial neurons and synapses and increase the depth of neural networks. This can be did, for example, with optical chips manufacture using silicon technology. This step is to take in the EU joint project ‘Fun-COMP’ by using foundry processing for the production of nanochips, says co-author and leader of the Fun-COMP project, Prof. C. David Wright from the University of Exeter.