93 research outputs found
Resonate and Fire Neuron with Fixed Magnetic Skyrmions
In the brain, the membrane potential of many neurons oscillates in a
subthreshold damped fashion and fire when excited by an input frequency that
nearly equals their eigen frequency. In this work, we investigate theoretically
the artificial implementation of such "resonate-and-fire" neurons by utilizing
the magnetization dynamics of a fixed magnetic skyrmion in the free layer of a
magnetic tunnel junction (MTJ). To realize firing of this nanomagnetic
implementation of an artificial neuron, we propose to employ voltage control of
magnetic anisotropy or voltage generated strain as an input (spike or
sinusoidal) signal, which modulates the perpendicular magnetic anisotropy
(PMA). This results in continual expansion and shrinking (i.e. breathing) of a
skyrmion core that mimics the subthreshold oscillation. Any subsequent input
pulse having an interval close to the breathing period or a sinusoidal input
close to the eigen frequency drives the magnetization dynamics of the fixed
skyrmion in a resonant manner. The time varying electrical resistance of the
MTJ layer due to this resonant oscillation of the skyrmion core is used to
drive a Complementary Metal Oxide Semiconductor (CMOS) buffer circuit, which
produces spike outputs. By rigorous micromagnetic simulation, we investigate
the interspike timing dependence and response to different excitatory and
inhibitory incoming input pulses. Finally, we show that such resonate and fire
neurons have potential application in coupled nanomagnetic oscillator based
associative memory arrays
Energy Efficient Spintronic Device for Neuromorphic Computation
Future computing will require significant development in new computing device paradigms. This is motivated by CMOS devices reaching their technological limits, the need for non-Von Neumann architectures as well as the energy constraints of wearable technologies and embedded processors. The first device proposal, an energy-efficient voltage-controlled domain wall device for implementing an artificial neuron and synapse is analyzed using micromagnetic modeling. By controlling the domain wall motion utilizing spin transfer or spin orbit torques in association with voltage generated strain control of perpendicular magnetic anisotropy in the presence of Dzyaloshinskii-Moriya interaction (DMI), different positions of the domain wall are realized in the free layer of a magnetic tunnel junction to program different synaptic weights. Additionally, an artificial neuron can be realized by combining this DW device with a CMOS buffer. The second neuromorphic device proposal is inspired by the brain. Membrane potential of many neurons oscillate in a subthreshold damped fashion and fire when excited by an input frequency that nearly equals their Eigen frequency. We investigate theoretical implementation of such “resonate-and-fire” neurons by utilizing the magnetization dynamics of a fixed magnetic skyrmion based free layer of a magnetic tunnel junction (MTJ). Voltage control of magnetic anisotropy or voltage generated strain results in expansion and shrinking of a skyrmion core that mimics the subthreshold oscillation. Finally, we show that such resonate and fire neurons have potential application in coupled nanomagnetic oscillator based associative memory arrays
Neuromorphic weighted sum with magnetic skyrmions
Integrating magnetic skyrmion properties into neuromorphic computing promises
advancements in hardware efficiency and computational power. However, a
scalable implementation of the weighted sum of neuron signals, a core operation
in neural networks, has yet to be demonstrated. In this study, we exploit the
non-volatile and particle-like characteristics of magnetic skyrmions, akin to
synaptic vesicles and neurotransmitters, to perform this weighted sum operation
in a compact, biologically-inspired manner. To this aim, skyrmions are
electrically generated in numbers proportional to the input with an efficiency
given by a non-volatile weight. These chiral particles are then directed using
localized current injections to a location where their presence is quantified
through non-perturbative electrical measurements. Our experimental
demonstration, currently with two inputs, can be scaled to accommodate multiple
inputs and outputs using a crossbar array design, potentially nearing the
energy efficiency observed in biological systems.Comment: 12 pages, 5 figure
Multilayer Ferromagnetic Spintronic Devices for Neuromorphic Computing Applications
Spintronics has gone through substantial progress due to its applications in
energy-efficient memory, logic and unconventional computing paradigms.
Multilayer ferromagnetic thin films are extensively studied for understanding
the domain wall and skyrmion dynamics. However, most of these studies are
confined to the materials and domain wall/skyrmion physics. In this paper, we
present the experimental and micromagnetic realization of a multilayer
ferromagnetic spintronic device for neuromorphic computing applications. The
device exhibits multilevel resistance states and the number of resistance
states increases with lowering temperature. This is supported by the multilevel
magnetization behavior observed in the micromagnetic simulations. Furthermore,
the evolution of resistance states with spin-orbit torque is also explored in
experiments and simulations. Using the multi-level resistance states of the
device, we propose its applications as a synaptic device in hardware neural
networks and study the linearity performance of the synaptic devices. The
neural network based on these devices is trained and tested on the MNIST
dataset using a supervised learning algorithm. The devices at the chip level
achieve 90\% accuracy. Thus, proving its applications in neuromorphic
computing. Furthermore, we lastly discuss the possible application of the
device in cryogenic memory electronics for quantum computers
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