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@MASTERSTHESIS{Zhao:1038905,
author = {Zhao, Wei},
title = {{A}lgorithm {D}evelopment {F}or {A}n{A}utomated {M}emristor
{C}ontrol {O}n {AN}euromorphic {D}emonstrator {C}hip},
school = {TU Munich},
type = {Masterarbeit},
reportid = {FZJ-2025-01715},
pages = {161},
year = {2024},
note = {Masterarbeit, TU Munich, 2024},
abstract = {Advancements on in-memory technology have positioned
memristors at the fore-front of non-volatile memory
applications, necessitating precise control mecha-nisms to
accurately program memristor cells to their respective
states. Whilecommercial memristor applications primarily
focus on binary data storage, theunique requirements of
neuromorphic computing necessitate the ability to handlea
wide range of analog values for synaptic emulation. This
thesis explores theutilization of a RISC-V processor and
pulse-width modulation generators to con-figure registers
for analog conductance control within a crossbar memristor
arrayarchitecture. The system incorporates mechanisms to
read back the programmedresistance values using an
analog-to-digital converter, compare these resistance
val-ues to the expected outcomes, and minimize any
discrepancies, thereby facilitatingaccurate voltage and
current mode operations.The core contribution of this
research is the development of a flexible and effi-cient
control algorithm, along with an error correction algorithm
specifically de-signed for the RISC-V architecture. By
leveraging these algorithms, the systemcan dynamically
adjust and correct the states of memristor cells, ensuring
highprecision and reliability in memory operations tailored
for neuromorphic comput-ing.To validate the control signals
generated by the proposed algorithms, a
UniversalVerification Methodology Framework testbench is
employed. This comprehensiveverification process ensures
that the control mechanisms are accurate and reliablebefore
proceeding to hardware implementation.The results from our
study indicate significant enhancements in control
effi-ciency, demonstrating the potential for seamless
integration of RISC-V processorswith memristor technology.
This integration paves the way for advanced non-volatile
memory solutions that are both robust and highly efficient,
marking anotable step forward in the field of memory
technology and specifically addressingthe needs of
neuromorphic computing.},
cin = {PGI-4 / ZEA-2},
cid = {I:(DE-Juel1)PGI-4-20110106 / I:(DE-Juel1)ZEA-2-20090406},
pnm = {5234 - Emerging NC Architectures (POF4-523) / BMBF
16ME0398K - Verbundprojekt: Neuro-inspirierte Technologien
der künstlichen Intelligenz für die Elektronik der Zukunft
- NEUROTEC II - (BMBF-16ME0398K)},
pid = {G:(DE-HGF)POF4-5234 / G:(DE-82)BMBF-16ME0398K},
typ = {PUB:(DE-HGF)19},
doi = {10.34734/FZJ-2025-01715},
url = {https://juser.fz-juelich.de/record/1038905},
}