Prospects for non-linear memristors as so-far missing core hardware element for transferless data computing and storage
<|reference_start|>Prospects for non-linear memristors as so-far missing core hardware element for transferless data computing and storage: We like and need Information and Communications Technologies (ICT) for data processing. This is measureable in the exponential growth of data processed by ICT, e.g. ICT for cryptocurrency mining and search engines. So far, the energy demand for computing technology has increased by a factor of 1.38 every ten years due to the exponentially increasing use of ICT systems as computing devices. The energy consumption of ICT systems is expected to rise from 1500 TWh (8% of global electricity consumption) in 2010 to 5700 TWh (14% of global electricity consumption) in 2030. A large part of this energy is required for the continuous data transfer between the separated memory and processor units which constitute the main components of ICT computing devices in von-Neumann architecture. This at the same time massively slows down the computing power of ICT systems in the von-Neumann architecture. In addition, due to the increasing complexity of AI compute algorithms, since 2010 the AI training compute time demand for computing technology increases tenfold every year, for example in the period from 2010 to 2020 from 1x10^{-6} to 1x10^{+4} Petaflops/Day. It has been theoretically predicted that ICT systems in the neuromorphic computer architecture will circumvent all of this through the use of merged memory and processor units. However, the core hardware element for this has not yet been realized so far. In this work we discuss the prespectives for non-linear resistive switches as the core hardware element for merged memory and processor units in neuromorphic computers.<|reference_end|>
@article{schmidt2024prospects,
    title={Prospects for non-linear memristors as so-far missing core hardware
  element for transferless data computing and storage},
    author={Heidemarie Schmidt},
    journal={under review 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.20051},
    primaryClass={cs.ET}
}