<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>