**2.1 Neuromorphic computing history**

The development of the perceptron in 1958 served as the forerunner of the artificial neurons employed in modern neural networks. With the inadequate understanding of the brain's inner workings at the time, the perceptron was an amateurish attempt to imitate some aspects of organic neural networks. The U.S. Navy planned to use the perceptron as a piece of mechanical hardware that was specifically designed for picture identification. Before it was understood that the technology could not perform the required job, it was subject to a great deal of hype.

Carver Mead, a professor at Caltech, first proposed neuromorphic computing in the 1980s. Mead's description of the first analogue silicon retina presaged a brand-new class of physical calculations that were motivated by the neural paradigm. Mead is also stated as believing that, given a thorough grasp of how the nervous system functions, nothing the human nervous system does cannot be replicated by computers in an article about neural computation in analogue VLSI.

However, the ubiquitous and expanding usage of AI, machine learning, and neural networks in consumer and enterprise technologies might be partly blamed for the recent investment and enthusiasm surrounding neuromorphic research. It can also be mainly related to the perception among many IT specialists that Moore's law is coming to an end. According to Moore's Law, a chip can accommodate twice as many micro components each year while maintaining the same price.

Major chip manufacturers like IBM and Intel are paying close attention to neuromorphic computing because it has the potential to get around conventional architectures and achieve radically higher levels of efficiency. In fact, Intel launched Moore's Law which was about to come to an end in 2017.

Moore's Law by condensing Gordon Moore's ideas has been launched in 2013, was also quoted as saying, "Going to multicore chips helped, but now we are up to eight cores and it does not look like we can go much further. People have to crash into the wall before they pay attention." This belief supports the idea that there are ups and downs in the hype and popular discourse surrounding artificial intelligence, with periods of low interest sometimes referred to as AI winters and times of high interest frequently brought on by an urgent issue that needs to be resolved, in this case the end of Moore's Law. the integration of neuromorphic chips and smart cockpits and explore related fields with BMW has been announced by SynSense The world's leading neuromorphic intelligence and solutions provider.
