Abstract
This chapter provides with an overview of the motivation and direction for neuro-memristive computing hardware. The emergence of deep learning technologies has been largely attributed to the convergence in the growth on computational capabilities, and that of the large availability of the data resulting from Internet of things applications. The need to have higher computational capabilities enforces the need to have low power solutions and smaller devices. However, the physical limits of CMOS device and process technologies pushed us in the recent years to think beyond CMOS era computing. A promising solutions is a class of emerging devices called memristors, that can naturally blend as a viable computing device to implement neural computations that extend the capabilities of exiting computing hardware. The full potential of neuro-memristive systems is yet to be completely realised and could provide ways to develop higher level of socially engineered machine cognition.
| Original language | English |
|---|---|
| Title of host publication | Modeling and Optimization in Science and Technologies |
| Publisher | Springer Verlag |
| Pages | 3-12 |
| Number of pages | 10 |
| DOIs | |
| Publication status | Published - Jan 1 2020 |
Publication series
| Name | Modeling and Optimization in Science and Technologies |
|---|---|
| Volume | 14 |
| ISSN (Print) | 2196-7326 |
| ISSN (Electronic) | 2196-7334 |
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ASJC Scopus subject areas
- Modelling and Simulation
- Medical Assisting and Transcription
- Applied Mathematics
Cite this
Introduction to neuro-memristive systems. / James Pappachen, Alex.
Modeling and Optimization in Science and Technologies. Springer Verlag, 2020. p. 3-12 (Modeling and Optimization in Science and Technologies; Vol. 14).Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Introduction to neuro-memristive systems
AU - James Pappachen, Alex
PY - 2020/1/1
Y1 - 2020/1/1
N2 - This chapter provides with an overview of the motivation and direction for neuro-memristive computing hardware. The emergence of deep learning technologies has been largely attributed to the convergence in the growth on computational capabilities, and that of the large availability of the data resulting from Internet of things applications. The need to have higher computational capabilities enforces the need to have low power solutions and smaller devices. However, the physical limits of CMOS device and process technologies pushed us in the recent years to think beyond CMOS era computing. A promising solutions is a class of emerging devices called memristors, that can naturally blend as a viable computing device to implement neural computations that extend the capabilities of exiting computing hardware. The full potential of neuro-memristive systems is yet to be completely realised and could provide ways to develop higher level of socially engineered machine cognition.
AB - This chapter provides with an overview of the motivation and direction for neuro-memristive computing hardware. The emergence of deep learning technologies has been largely attributed to the convergence in the growth on computational capabilities, and that of the large availability of the data resulting from Internet of things applications. The need to have higher computational capabilities enforces the need to have low power solutions and smaller devices. However, the physical limits of CMOS device and process technologies pushed us in the recent years to think beyond CMOS era computing. A promising solutions is a class of emerging devices called memristors, that can naturally blend as a viable computing device to implement neural computations that extend the capabilities of exiting computing hardware. The full potential of neuro-memristive systems is yet to be completely realised and could provide ways to develop higher level of socially engineered machine cognition.
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U2 - 10.1007/978-3-030-14524-8_1
DO - 10.1007/978-3-030-14524-8_1
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T3 - Modeling and Optimization in Science and Technologies
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PB - Springer Verlag
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