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@INPROCEEDINGS{Siegel:1028733,
author = {Siegel, Sebastian and Bouhadjar, Younes and Ziegler, Tobias
and Waser, R. and Dittmann, Regina and Wouters, Dirk},
title = {{M}em{S}piking{TM}: {N}euromorphic sequence learning with
memristive in-memory computing - from algorithm to hardware
demonstration},
reportid = {FZJ-2024-04781},
year = {2024},
abstract = {Information processing in the neo-cortex happens in a
sequential manner and sequence learning is considered a key
functionality of the human brain. Even though there are
machine learning solutions to these problems, they, unlike
the biological brain, often require large amounts of
training data and suffer from a high energy consumption.
Therefore, in this project we take the neuromorphic approach
of bringing the biological principles of sparse neural
activity and in-memory computing into electronic hardware.
Thereby, our goal is to achieve a robust and
energy-efficient solution for sequence learning.The
Hierarchical Temporal Memory[1] concept and its biologically
plausible version, SpikingTM[2], describe a possible
algorithm for sequence learning in the neo-cortex. We prove
that this algorithm can operate with memristive synapses[3].
Memristive devices are an emerging non-volatile memory and a
prominent candidate for in-memory computing substrates. In
order to fully leverage the possibilities of these devices,
we adapt the SpikingTM algorithm and create a complete
analog / mixed signal system model around a synaptic array
of memristive devices[4]. We demonstrate sequence learning
by sparse neural activity and showcase that the use of
memristive devices leads to a significant gain of energy
efficiency. Lastly, we validate the system with real
memristive synaptic arrays on a custom nanometer CMOS
demonstrator chip by performing complex sequence learning
tasks with our memristive algorithm (MemSpikingTM) on
hardware[5].This project shows the complete neuromorphic
journey from a bio-plausible algorithm for the brain
functionality over a hardware-aware adaption for emerging
memristive device technology and a complete system model to
a successful hardware demonstration. We showcase that by
combining the biological principles of sparse activity and
connectivity with a memristive in-memory computing
substrate, we can fulfil the promise of robust brain-like
functionality and energy efficiency. [1] S. Ahmad and J.
Hawkins, arXiv preprint arXiv:1503.07469,2015[2] Y.
Bouhadjar et al., PLOS Computational Biology, 18.6, 2022[3]
Y. Bouhadjar et al., Neuromorph. Comput. Eng., 3.3, 2023[4]
S. Siegel et al., Neuromorph. Comput. Eng., 3.2, 2023[5] S.
Siegel et al., Proceedings of the 2023 NICE Conference,
2023},
month = {Jun},
date = {2024-06-03},
organization = {International Conference on
Neuromorphic Computing and Engineering,
Aachen (Fed Rep Germany), 3 Jun 2024 -
6 Jun 2024},
subtyp = {After Call},
cin = {PGI-14},
cid = {I:(DE-Juel1)PGI-14-20210412},
pnm = {5234 - Emerging NC Architectures (POF4-523)},
pid = {G:(DE-HGF)POF4-5234},
typ = {PUB:(DE-HGF)24},
doi = {10.34734/FZJ-2024-04781},
url = {https://juser.fz-juelich.de/record/1028733},
}