2021 Roadmap on Neuromorphic Computing and Engineering
| dc.contributor.author | Mazumder, Arnab Neelim | |
| dc.contributor.author | Hosseini, Morteza | |
| dc.contributor.author | Mohsenin, Tinoosh | |
| dc.contributor.author | et al. | |
| dc.date.accessioned | 2021-06-04T20:04:25Z | |
| dc.date.available | 2021-06-04T20:04:25Z | |
| dc.date.issued | 2021-05-12 | |
| dc.description | Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano, Carlo Ricciardi, Shi-Jun Liang, Feng Miao, Mario Lanza, Tyler J. Quill, Scott T. Keene, Alberto Salleo, Julie Grollier, Danijela Marković, Alice Mizrahi, Peng Yao, J. Joshua Yang, Giacomo Indiveri, John Paul Strachan, Suman Datta, Elisa Vianello, Alexandre Valentian, Johannes Feldmann, Xuan Li, Wolfram H.P. Pernice, Harish Bhaskaran, Emre Neftci, Srikanth Ramaswamy, Jonathan Tapson, Franz Scherr, Wolfgang Maass, Priyadarshini Panda, Youngeun Kim, Gouhei Tanaka, Simon Thorpe, Chiara Bartolozzi, Thomas A. Cleland, Christoph Posch, Shih-Chii Liu, Arnab Neelim Mazumder, Morteza Hosseini, Tinoosh Mohsenin, Elisa Donati, Silvia Tolu, Roberto Galeazzi, Martin Ejsing Christensen, Sune Holm, Daniele Ielmini, N. Pryds | en_US |
| dc.description.abstract | Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In this architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex and unstructured data as our brain does. Neuromorphic computing systems are aimed at addressing these needs. The human brain performs about 10^15 calculations per second using 20W and a 1.2L volume. By taking inspiration from biology, new generation computers could have much lower power consumption than conventional processors, could exploit integrated non-volatile memory and logic, and could be explicitly designed to support dynamic learning in the context of complex and unstructured data. Among their potential future applications, business, health care, social security, disease and viruses spreading control might be the most impactful at societal level. This roadmap envisages the potential applications of neuromorphic materials in cutting edge technologies and focuses on the design and fabrication of artificial neural systems. The contents of this roadmap will highlight the interdisciplinary nature of this activity which takes inspiration from biology, physics, mathematics, computer science and engineering. This will provide a roadmap to explore and consolidate new technology behind both present and future applications in many technologically relevant areas. | en_US |
| dc.description.uri | https://arxiv.org/abs/2105.05956 | en_US |
| dc.format.extent | 153 pages | en_US |
| dc.genre | journal articles preprints | en_US |
| dc.identifier | doi:10.13016/m2n6ua-rira | |
| dc.identifier.citation | Mazumder, Arnab Neelim; Hosseini, Morteza; Mohsenin, Tinoosh; et al.; 2021 Roadmap on Neuromorphic Computing and Engineering; Neuromorphic Computing and Engineering, May 12, 2021; https://arxiv.org/abs/2105.05956 | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/21686 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IOP Publishing | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.title | 2021 Roadmap on Neuromorphic Computing and Engineering | en_US |
| dc.type | Text | en_US |