An RRAM-based building block for reprogrammable non-uniform sampling ADCs
Autor(en):
Zusammenfassung
RRAM devices have recently seen wide-spread adoption into applications such as neural networks and storage elements since their inherent non-volatility and multi-bit-capability renders them a possible candidate for mitigating the von-Neumann bottleneck. Researchers often face difficulties when developing edge devices, since dealing with sensors detecting parameters such as humidity or temperature often requires large and power-consuming ADCs. We propose a possible mitigation, namely using a RRAM device in combination with a comparator circuit to form a basic block for threshold detection. This can be expanded towards programmable non-uniform sampling ADCs, significantly reducing both area and power consumption since significantly smaller bit-resolutions are required. We demonstrate how a comparator circuit designed in 130 nm technology can be reprogrammed by programming the incorporated RRAM device. Our proposed building block consumes 83 µW.
- Vollständige Referenz
- BibTeX
Vishwakarma, A., Fritscher, M., Hagelauer, A. & Reichenbach, M.,
(2023).
An RRAM-based building block for reprogrammable non-uniform sampling ADCs.
it - Information Technology: Vol. 65, No. 1-2.
Berlin:
De Gruyter.
(S. 39-51).
DOI: 10.1515/itit-2023-0021
@article{mci/Vishwakarma2023,
author = {Vishwakarma, Abhinav AND Fritscher, Markus AND Hagelauer, Amelie AND Reichenbach, Marc},
title = {An RRAM-based building block for reprogrammable non-uniform sampling ADCs},
journal = {it - Information Technology},
volume = {65},
number = {1-2},
year = {2023},
,
pages = { 39-51 } ,
doi = { 10.1515/itit-2023-0021 }
}
author = {Vishwakarma, Abhinav AND Fritscher, Markus AND Hagelauer, Amelie AND Reichenbach, Marc},
title = {An RRAM-based building block for reprogrammable non-uniform sampling ADCs},
journal = {it - Information Technology},
volume = {65},
number = {1-2},
year = {2023},
,
pages = { 39-51 } ,
doi = { 10.1515/itit-2023-0021 }
}
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Mehr Information
ISSN: 2196-7032
Datum: 2023
Sprache:
(en)
(en)
Typ: Text/Journal Article

