A scientist from Indian institute of Science (ISC), Bengaluru, has worked on applications of emerging materials that can help computers mimic functions that the brain can perform rapidly. “While several synaptic device proposals are available in literature, none helps bridge the gap between biological neural networks and their artificial counterpart. Our work has shown that developing systems that can mimic brain- like function is achievable. It can help computers perform cognitive functions, identify people from a crowd, distinguish smell as well as learn and make decisions,” IISc’s Professor Mayank Shrivastava, who is  also a recipient of the Swarna Jayanti Fellowship 2020-21, said.

Since their inception, advanced computing systems have been using von Neumann architecture, which uses physically separated processing and memory blocks. While it has been the most cost-effective approach to date, physical separation of memory from the processing blocks has become the rate limiter for pushing the computational capabilities of advanced nano-electronic systems.

Advertisements

Besides,von Neumann’s architecture fails in real- time processing of information that the human brain can process in a fraction of a second. Keeping these gaps in mind, an alternative architecture, inspired by the organisation of neurons (processing unit) and synapses (memory) in the human brain that emulates brain-like computing behaviour, have been explored significantly in the last decade.

A significant amount of research to understand the fundamental mechanisms of the brain and explorations of various novel memory architectures is now giving the engineering community confidence that developing systems that can mimic brain-like function is an achievable goal for the coming decades. A key element of such an architecture is a memory device called the artificial synapse, which, however, must work on biological / synaptic principles. Professor Srivastava is exploring materials such as Gallium Nitride (GaN), atomically thin two-dimensional materials like Graphene, and Transition metal dichalcogenides (TMDCs), for various electronics, power devices, electro-optic, Thz, memory, and quantum applications. TMDCC is extending the capabilities of memory devices to work on biological/ synaptic principles and bridge the gap between biological neural networks and their artificial counterpart.

Advertisements