<p>With the Moore’s Law era coming to an end, much attention has
been given to novel nanoelectronic devices as a key driving force behind
technological innovation. Utilizing the inherent device physics of
nanoelectronic components, for sensory and computational tasks have proven to
be useful in reducing the area and energy requirements of the underlying
hardware fabrics. In this work we demonstrate how the intrinsic noise present
in nano magnetic devices can pave the pathway for energy efficient neuromorphic
hardware. Furthermore, we illustrate how the unique magnetic properties of such
devices can be leveraged for accurate estimation of environmental magnetic
fields. We focus on spintronic technologies in particular, due to the low current
and energy requirements in contrast to traditional CMOS technologies.</p><p>Image
segmentation is a crucial pre-processing stage used in many object
identification tasks that involves simplifying the representation of an image
so it can be conveniently analyzed in the later stages of a problem. This is
achieved through partitioning a complicated image into specific groups based on
color, intensity or texture of the pixels of that image. Locally Excitatory
Globally Inhibitory Oscillator Network or LEGION is one such segmentation
algorithm, where synchronization and desynchronization between coupled
oscillators are used for segmenting an image. In this
work we present an energy efficient and scalable hardware implementation of LEGION
using stochastic Magnetic Tunnel Junctions that leverage the fast parallel</p><p>
nature
of the algorithm. We demonstrate that the proposed hardware is capable of segmenting
binary and gray-scale images with multiple objects more efficiently than<br>
existing
hardware implementations. </p><p>It is understood that the underlying device physics
of spin devices can be used for emulating the functionality of a spiking
neuron. Stochastic spiking neural networks based on nanoelectronic spin devices
can be a possible pathway of achieving brain-like compact and energy-efficient
cognitive intelligence. Current computational models attempt to exploit the
intrinsic device stochasticity of nanoelectronic synaptic or neural components
to perform learning and inference. However, there has been limited analysis on
the scaling effect of stochastic spin devices and its impact on the operation
of such stochastic networks at the system level. Our work attempts to explore
the design space and analyze the performance of nanomagnet based stochastic neuromorphic
computing architectures, for magnets with different barrier heights. We illustrate
how the underlying network architecture must be modified to account for the
random telegraphic switching behavior displayed by magnets as they are scaled into
the superparamagnetic regime.<br></p><p>Next we investigate how the magnetic properties
of spin devices can be utilized for real world sensory applications. Magnetic
Tunnel Junctions can efficiently translate variations in external magnetic
fields into variations in electrical resistance. We couple this property of
Magnetic Tunnel Junctions with Amperes law to design a non-invasive sensor to
measure the current flowing through a wire. We demonstrate how undesirable
effects of thermal noise and process variations can be suppressed through novel
analog and digital signal conditioning techniques to obtain reliable and
accurate current measurements. Our results substantiate that the proposed
noninvasive current sensor surpass other state-of-the-art technologies in terms
of noise and accuracy.<br></p><br>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/15062343 |
| Date | 27 July 2021 |
| Creators | Chamika M Liyanagedera (11191896) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Intelligent_Sensing_and_Energy_Efficient_Neuromorphic_Computing_using_Magneto-Resistive_Devices/15062343 |
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