86 research outputs found
Stochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference
Probabilistic inference from real-time input data is becoming increasingly
popular and may be one of the potential pathways at enabling cognitive
intelligence. As a matter of fact, preliminary research has revealed that
stochastic functionalities also underlie the spiking behavior of neurons in
cortical microcircuits of the human brain. In tune with such observations,
neuromorphic and other unconventional computing platforms have recently started
adopting the usage of computational units that generate outputs
probabilistically, depending on the magnitude of the input stimulus. In this
work, we experimentally demonstrate a spintronic device that offers a direct
mapping to the functionality of such a controllable stochastic switching
element. We show that the probabilistic switching of Ta/CoFeB/MgO
heterostructures in presence of spin-orbit torque and thermal noise can be
harnessed to enable probabilistic inference in a plethora of unconventional
computing scenarios. This work can potentially pave the way for hardware that
directly mimics the computational units of Bayesian inference
Stochastic Domain Wall-Magnetic Tunnel Junction Artificial Neurons for Noise-Resilient Spiking Neural Networks
The spatiotemporal nature of neuronal behavior in spiking neural networks
(SNNs) make SNNs promising for edge applications that require high energy
efficiency. To realize SNNs in hardware, spintronic neuron implementations can
bring advantages of scalability and energy efficiency. Domain wall (DW) based
magnetic tunnel junction (MTJ) devices are well suited for probabilistic neural
networks given their intrinsic integrate-and-fire behavior with tunable
stochasticity. Here, we present a scaled DW-MTJ neuron with voltage-dependent
firing probability. The measured behavior was used to simulate a SNN that
attains accuracy during learning compared to an equivalent, but more
complicated, multi-weight (MW) DW-MTJ device. The validation accuracy during
training was also shown to be comparable to an ideal leaky integrate and fire
(LIF) device. However, during inference, the binary DW-MTJ neuron outperformed
the other devices after gaussian noise was introduced to the Fashion-MNIST
classification task. This work shows that DW-MTJ devices can be used to
construct noise-resilient networks suitable for neuromorphic computing on the
edge.Comment: 10 pages, 4 figure
Leveraging Probabilistic Switching in Superparamagnets for Temporal Information Encoding in Neuromorphic Systems
Brain-inspired computing - leveraging neuroscientific principles underpinning
the unparalleled efficiency of the brain in solving cognitive tasks - is
emerging to be a promising pathway to solve several algorithmic and
computational challenges faced by deep learning today. Nonetheless, current
research in neuromorphic computing is driven by our well-developed notions of
running deep learning algorithms on computing platforms that perform
deterministic operations. In this article, we argue that taking a different
route of performing temporal information encoding in probabilistic neuromorphic
systems may help solve some of the current challenges in the field. The article
considers superparamagnetic tunnel junctions as a potential pathway to enable a
new generation of brain-inspired computing that combines the facets and
associated advantages of two complementary insights from computational
neuroscience -- how information is encoded and how computing occurs in the
brain. Hardware-algorithm co-design analysis demonstrates accuracy of
a state-compressed 3-layer spintronics enabled stochastic spiking network on
the MNIST dataset with high spiking sparsity due to temporal information
encoding
A spintronic Huxley-Hodgkin-analogue neuron implemented with a single magnetic tunnel junction
Spiking neural networks aim to emulate the brain's properties to achieve
similar parallelism and high-processing power. A caveat of these neural
networks is the high computational cost to emulate, while current proposals for
analogue implementations are energy inefficient and not scalable. We propose a
device based on a single magnetic tunnel junction to perform neuron firing for
spiking neural networks without the need of any resetting procedure. We
leverage two physics, magnetism and thermal effects, to obtain a bio-realistic
spiking behavior analogous to the Huxley-Hodgkin model of the neuron. The
device is also able to emulate the simpler Leaky-Integrate and Fire model.
Numerical simulations using experimental-based parameters demonstrate firing
frequency in the MHz to GHz range under constant input at room temperature. The
compactness, scalability, low cost, CMOS-compatibility, and power efficiency of
magnetic tunnel junctions advocate for their broad use in hardware
implementations of spiking neural networks.Comment: 23 pages, 6 figures, 2 table
Spiking Dynamics in Dual Free Layer Perpendicular Magnetic Tunnel Junctions
Spintronic devices have recently attracted a lot of attention in the field of
unconventional computing due to their non-volatility for short and long term
memory, non-linear fast response and relatively small footprint. Here we report
how voltage driven magnetization dynamics of dual free layer perpendicular
magnetic tunnel junctions enable to emulate spiking neurons in hardware. The
output spiking rate was controlled by varying the dc bias voltage across the
device. The field-free operation of this two terminal device and its robustness
against an externally applied magnetic field make it a suitable candidate to
mimic neuron response in a dense Neural Network (NN). The small energy
consumption of the device (4-16 pJ/spike) and its scalability are important
benefits for embedded applications. This compact perpendicular magnetic tunnel
junction structure could finally bring spiking neural networks (SNN) to
sub-100nm size elements
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