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@INPROCEEDINGS{Siegel:1006783,
author = {Siegel, Sebastian and Ziegler, Tobias and Bouhadjar, Younes
and Tetzlaff, Tom and Waser, Rainer and Dittmann, Regina and
Wouters, Dirk},
title = {{D}emonstration of neuromorphic sequence learning on a
memristive array},
publisher = {ACM New York, NY, USA},
reportid = {FZJ-2023-01836},
pages = {1},
year = {2023},
comment = {Neuro-Inspired Computational Elements Conference :
[Proceedings] - ACM New York, NY, USA, 2023. - ISBN
9781450399470 - doi:10.1145/3584954.3585000},
booktitle = {Neuro-Inspired Computational Elements
Conference : [Proceedings] - ACM New
York, NY, USA, 2023. - ISBN
9781450399470 -
doi:10.1145/3584954.3585000},
abstract = {Sequence learning and prediction are considered principle
computations performed by biological brains. Machine
learning algorithms solve this type of task, but they
require large amounts of training data and a substantial
energy budget. An approach to overcome these issues and
enable sequence learning with brain-like performance is
neuromorphic hardware with brain-inspired learning
algorithms. The Hierarchical Temporal Memory (HTM) is an
algorithm inspired by the working principles of the
neocortex and is able to learn and predict continuous
sequences of elements. In a previous study, we showed that
memristive devices, an emerging non-volatile memory
technology, that is considered for energy efficient
neuromorphic hardware, can be used as synapses in a
biologically plausible version of the temporal memory
algorithm of the HTM model. We subsequently presented a
simulation study of an analog-mixed signal memristive
hardware architecture that can implement the temporal
learning algorithm. This architecture, which we refer to as
MemSpikingTM, is based on a memristive crossbar array and a
control circuitry implementing the neurons and the learning
mechanism. In the study presented here, we demonstrate the
functionality of the MemSpikingTM algorithm on a real
memristive crossbar array, taped out in a commercially
available 130nm CMOS technology node co-integrated with HfO
based memristive devices. We explain the algorithm and the
functionality of the crossbar array and peripheral circuitry
and finally demonstrate context-dependent sequence learning
using high-order sequences.},
month = {Apr},
date = {2023-04-03},
organization = {NICE 2023: Neuro-Inspired
Computational Elements Conference, San
Antonio TX USA (USA), 3 Apr 2023 - 7
Apr 2023},
cin = {PGI-7 / PGI-10 / JARA-FIT / INM-6 / PGI-15},
cid = {I:(DE-Juel1)PGI-7-20110106 / I:(DE-Juel1)PGI-10-20170113 /
$I:(DE-82)080009_20140620$ / I:(DE-Juel1)INM-6-20090406 /
I:(DE-Juel1)PGI-15-20210701},
pnm = {5233 - Memristive Materials and Devices (POF4-523) / BMBF
16ME0399 - Verbundprojekt: Neuro-inspirierte Technologien
der künstlichen Intelligenz für die Elektronik der Zukunft
- NEUROTEC II - (BMBF-16ME0399) / BMBF 16ME0398K -
Verbundprojekt: Neuro-inspirierte Technologien der
künstlichen Intelligenz für die Elektronik der Zukunft -
NEUROTEC II - (BMBF-16ME0398K)},
pid = {G:(DE-HGF)POF4-5233 / G:(DE-82)BMBF-16ME0399 /
G:(DE-82)BMBF-16ME0398K},
typ = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
UT = {WOS:001089568500017},
doi = {10.1145/3584954.3585000},
url = {https://juser.fz-juelich.de/record/1006783},
}