Innovation

At the heart of all artificial intelligence applications are mathematical models called neural networks. The biological structure of the human brain, which is made up of interconnected nodes, inspired these models. Neural networks can be mathematically trained by tuning their internal structure until they are capable of human-level tasks, such as recognising our faces, interpreting medical images for diagnosis, and even driving our cars, much like our brain learns by constantly rearranging the connections between neurons. Having integrated devices capable of swiftly and efficiently completing the computations necessary in neural networks has thus become a major academic and corporate research priority. The development of the memristor in 2008 was one of the key game changers in the area. The memory-resistor, or memristor, is a device that adjusts its resistance based on a memory of a previous current. Scientists noticed very away that the unique behaviour of memristors was remarkably comparable to that of neural synapses (among many other applications). As a result, the memristor has become a crucial component in neuromorphic designs. Prof. Philip…