Összefoglalás.
Napjainkra az információs technológiák fejlődése elérte azt a szintet, ahol a
gyorsuló ütemben létrejövő adattömeg feldolgozásához már sok esetben elégtelenek
a klasszikus, Neumann-elvek alapján működő számítógépek. A jelenség újszerű
szoftveres megoldások, biológiai ihletésű algoritmusok, neurális hálózatok
elterjedéséhez vezetett, ám ezek hatékony alkalmazásához teljesen új hardveres
megoldások szükségesek. Jelen kézirat ilyen újszerű architektúrákhoz
fejlesztett, Si-mikrochip-alapú memóriatulajdonsággal rendelkező nanoméretű
áramköri elemek kísérleti eredményeit mutatja be, illetve azok egy-egy
specifikus információfeldolgozási feladatra történő alkalmazhatóságával
foglalkozik.
Summary.
Resistive switching memory devices, also known as memristors, are generally
metal-insulator-metal nanostructures whose conductivity can be varied via
electrical signals, enabling information storage in the value of the
conductivity. Based on this property, memristive devices provide a promising
platform for hardware-level encoding of large matrices. With a network of
memristors, computationally intensive vector-matrix operations can be performed
in a single step, considerably speeding up the operation of an artificial neural
network (ANN). Memristors can also serve as real physical building blocks for
biologically inspired algorithms through their neuromorphic properties. Via
building simple circuits, such devices can be used to create oscillators or
artificial neurons, which can be utilized for the implementation of oscillatory
neural networks (ONN) or spiking neural networks (SNN). Another interesting
feature of these memristive neuromorphic circuits is that they can be directly
used for information processing tasks at the edge of a network. Memristors
facilitate such edge computing applications which usually require
energy-efficient operation and small size of the processor unit. Edge computing
approaches have several advantages over centralized data processing from the
aspect of security, e.g., significantly reducing time latency of sending large
amounts of data to a central hub, and by processing sensitive data locally and
independently of the central servers.
Present work focuses on the experimental investigation of purpose-built nanoscale
memristive devices, revealing their physical processes. A superconducting
spectroscopy measurement technique is developed for the non-destructive
detection of atomic scale memristive filaments during device operation
(Török et al. 2020; Török et al. 2023). In addition, the
tunable stochasticity of the nucleation process is revealed by statistical
studies of the set process in silicon oxide memristors (Török et al.
2022). This finding provides a basis for the physical realization
of neural activation functions, stochastically firing neurons or
energy-efficient true random number generation. Finally, the applicability of
the investigated nanoscale memristive devices is illustrated through two
examples. The concept of a neuromorphic, memristor-based auditory sensing unit
is presented, leading towards medical application in a fully implantable
cochlear implant. Last, the feasibility of a hardware-level stochastic
optimization procedure is introduced (Fehérvári et al. 2023),
based entirely on memristive elements, utilizing tunable noise characteristics
of the devices (Sánta et al. 2021).
