Probably everyone who has ever used a computer probably knows that today’s consumer computers are far from perfect. Unlike our brains, they are not able to learn the most efficient processes and must repeat them at the same or lower speed over time. Moreover, they can send information from one place to another at one time. However, it could change the so-called neuromorphic chips, or neuromorphic computers made up of these chips.
In the spirit of the brain, neuromorphic chips are able to “learn” through architecture analogous simulating elements of the brain (neurons, synapses), which processes need to be performed more often and which less often, or more, can perform multiple operations at once.
SpiNNaker (Spiking Neural Network Architecture) is the latest iron in the development of similar computers, capable of executing over 200 trillion parallel operations – a figure of 12 zeros. However, even this set is still insufficient compared to what each of us wears in the skull. SpiNNaker, with all its capacity, reaches “only” a thousandth of a percent of the estimated speed of the human brain.
However, its authors from the united British-European team intend to gradually reduce the lead of homo sapiens – the next goal will be the development of SpiNNaker’s successor reaching perhaps a thousand times higher speed. Thus, such a machine could have one percent of the speed of the human brain, at least as it is possible to compare difficult to compare systems side by side.
What is the use of such operations? In addition to advances in the field of computer science, especially for simulations, respectively. rather, emulation of biological processes. In the summer of this year, SpiNNaker made its first emulation of tens of thousands of neurons linked into a single unit. His future simulations are expected to better understand the functioning of the human brain – and perhaps to better understand neurodegenerative diseases such as Alzheimer’s and Parkinson’s. In this case, the development of informatics goes hand in hand with the possibilities for medicine.
This does not mean that neuromorphic computers will only be useful for doctors. The advantage of parallel operation is the possibility to realize calculations several times more economically in terms of energy output. In other words, neuromorphic chips have the chance to reduce the current power demand of computers and thus their cooling.
Tomorrow’s neuromorphic chips could bring machine learning applications to mobile computers. It is a bit exaggerated to say that, similarly to PC owners, in the past, they purchased designed graphics cards, in the future, we can look forward to specialized “AI cards”.
The promise of dramatically increasing computing power in the coming decades is all the greater as the development of quantum computers, carbon nanotube computers, and other technologies continue to move forward along with neuromorphic computers. These could ensure that computing beyond 2030 could far exceed today’s capabilities. The paradox is that SpiNNaker itself is actually already slightly outdated in this respect – its construction took over 10 years and began in 2006. Technology development then began in the mid-1990s.
Neuromorphic chips like Spikey being developed today could well surpass it in aggregate. However, it may still be some time before they are assembled into usable supercomputers. After all, even SpiNNaker wasn’t built in a day.