dc.contributor.advisor |
Kulkarni, G.U. |
|
dc.contributor.author |
B, Bharath |
|
dc.date.accessioned |
2021-07-16T10:49:24Z |
|
dc.date.available |
2021-07-16T10:49:24Z |
|
dc.date.issued |
2020 |
|
dc.identifier.citation |
B, Bharath. 2020, Design and fabrication of neuromorphic devices towards emulating artificial intelligence, Ph.D thesis, Jawaharlal Nehru Centre for Advanced Scientific Research, Bengaluru |
en_US |
dc.identifier.uri |
https://libjncir.jncasr.ac.in/xmlui/handle/123456789/3125 |
|
dc.description |
Open access |
en_US |
dc.description.abstract |
Artificial Intelligence (AI) aims to simulate human intelligence in machines so that they can think like humans, and perform human-like cognition [1]. Although the present-day supercomputers can perform excellent calculations, they are very slow and inefficient compared to the human brain! [2,3] For example, a supercomputer takes around 500 s to emulate 5 s of human brain activity by consuming ~ MWs of energy [4]. This is much due to the conventional von Neumann computer architecture, where the memory and processing units are physically separate and are connected by limited interconnects known as bus bars [5,6]. During processing, data shuttles between these units, which makes the process slow and inefficient. Besides, limited transistor density in a processor chip also influences the computational ability. Although the advancing lithography technique is endeavoring to miniaturize transistors to the lowest dimension possible [7] to create a high density on board, the computational performance is still under satisfying. As the transistor scaling down approaches the theoretical limit, their packing density in a chip may not hold the famous Moore’s law any further [8]. |
en_US |
dc.language |
English |
en |
dc.language.iso |
English |
en_US |
dc.publisher |
Jawaharlal Nehru Centre for Advanced Scientific Research |
en_US |
dc.subject |
Neuromorphic devices |
en_US |
dc.title |
Design and fabrication of neuromorphic devices towards emulating artificial intelligence |
en_US |
dc.type |
Thesis |
en_US |
dc.type.qualificationlevel |
Doctoral |
en_US |
dc.type.qualificationname |
Ph.D |
en_US |
dc.publisher.department |
Chemistry and Physics of Materials Unit (CPMU) |
en_US |