The human brain is a complex system. As it goes to places no one else could and handles multiple tasks that could be difficult for other artificial systems. Due to these functions, human brain has long intrigue scientists who have conduct many studies to understand it.
But it is unpredictable.
But there’s something else that is remarkable about the human brain, it is incredibly energy-efficient.
Scientists are trying to understand it and draw inspiration to make future computer technologies.
The experts are focussing on the interconnect neuron function of brain because it holds as well as processes data.
Scientists expect brain-inspire computers would be more energy-efficient than the conventional ones we use today.
They also believe that these brain-inspire computers would be better at performing machine learning tasks.
The current focus on the neuron function is seep into the belief that combining storage and processing in a single electronic component type call as a memristor, which will allow scientists to achieve greater efficiency.
In conventional computers, data is move between the processor and the storage, the primary cause of higher energy consumption in machine learning applications.
To do this, the memristor will have to be modified.
Researchers at ETH Zurich, the University of Zurich and Empa have combine efforts to develop an innovative concept for memristor to allow it to be use in a far wider range of applications.
They have outline their research in a paper publish in the journal Nature Communication.
ETH postdoctoral fellow Rohit John explain in a statement that there are different operation modes for memristors.
The conventional memristors had to be configure in only one of these modes.
The new memristors can easily switch between two operation modes while in use.
Rohit John Said :
“These two operation modes are also found in the human brain,”.
For the study, the researchers test 25 of these new memristors and carry out 20,000 measurements with them.
These electronic devices will need to undergo further optimisation before they can be use in computer technology.
