The continuous scaling down of the metal-oxide-semiconductor field-effect transistor (MOSFET) has improved the performance of electronic appliances. Unfortunately, it has come to a stage where further scaling of the MOSFET is no longer possible due to the physical and the fabrication limitations. This has motivated researchers towards designing and fabricating novel devices that can replace MOSFET technology. Carbon Nanotube Field-Effect Transistors, Single Electron Tunneling Junctions, Nano-Magnetic Devices, and Spin Field-Effect Transistors are some prospective candidates that could replace MOSFET devices. In this dissertation, we have studied the computational performance of Nano−Magnetic Devices due to their attractive features such as room temperature operation, high density, robustness towards thermal noise, radiation hardened nature and low static power dissipation. In this work, we have established that data can be propagated in a causal fashion from a driver cell to the driven cells. We have fabricated a ferromagnetic wire architecture and used a magnetic force microscopy (MFM) tip to provide localized magnetic inputs. This experiment validated two important phenomena; (1) a clocking field is essential to propagate data and (2) upon removal of the clocking field data can be propagated according to the input data. Next, we have fabricated and captured MFM images of a nano-magnetic logic architecture that has computed the majority of seven binary variables. The architecture was designed by interconnecting three three-input majority logic gates with ferromagnetic and antiferromagnetic wire architectures. This seven input majority logic architecture can potentially implement eight different logic functions that could be configured in real-time. All eight functions could be configured by three control parameters in real-time (by writing logic one or zero to them). Even though we observed error-free operations in nano-magnetic logic architectures, it became clear that we needed better control (write/read/clock) over individual single layer nano-magnetic devices for successful long-term operation. To address the write/clock/read problems, we designed and fabricated amultilayer nano-magnetic device. We fabricated and performed a set of experiments with patterned multilayer stacks of Co/Cu/Ni80Fe20 with a bottom layer having a perpendicular magnetization to realize neighbor interactions between adjacent top layers of devices. Based on the MFM images, we conclude that dipolar coupling between the top layers of the neighboring devices can be exploited to construct three-input majority logic gates, antiferromagnetic and ferromagnetic wire architectures. Finally, we have experimentally demonstrated a magnetic system that could be used to solve quadratic optimization problems that arise in computer vision applications. We have harnessed the energy minimization nature of a magnetic system to directly solve a quadratic optimization process. We have fabricated a magnetic system corresponding to a real world image and have identified salient features with true positive rate more than 85%. These experimental results feature the potentiality of this unconventional computing method to develop a magnetic processor which solves such complex problems in few clock cycles.

<p>Deep ‘Analog Artificial Neural Networks’ (AANNs) perform complex classification problems with high accuracy. However, they rely on humongous amount of power to perform the calculations, veiling the accuracy benefits. The biological brain on the other hand is significantly more powerful than such networks and consumes orders of magnitude less power, indicating some conceptual mismatch. Given that the biological neurons are locally connected, communicate using energy efficient trains of spikes, and the behavior is non-deterministic, incorporating these effects in Artificial Neural Networks (ANNs) may drive us few steps towards a more realistic neural networks. </p> <p> </p> <p>Emerging devices can offer a plethora of benefits including power efficiency, faster operation, low area in a vast array of applications. For example, memristors and Magnetic Tunnel Junctions (MTJs) are suitable for high density, non-volatile Random Access Memories when compared with CMOS implementations. In this work, we analyze the possibility of harnessing the characteristics of such emerging devices, to achieve neuro-inspired solutions to intricate problems.</p> <p> </p> <p>We propose how the inherent stochasticity of nano-scale resistive devices can be utilized to realize the functionality of spiking neurons and synapses that can be incorporated in deep stochastic Spiking Neural Networks (SNN) for image classification problems. While ANNs mainly dwell in the aforementioned classification problem solving domain, they can be adapted for a variety of other applications. One such neuro-inspired solution is the Cellular Neural Network (CNN) based Boolean satisfiability solver. Boolean satisfiability (k-SAT) is an NP-complete (k≥3) problem that constitute one of the hardest classes of constraint satisfaction problems. We provide a proof of concept hardware based analog k-SAT solver that is built using MTJs. The inherent physics of MTJs, enhanced by device level modifications, is harnessed here to emulate the intricate dynamics of an analog, CNN based, satisfiability (SAT) solver. </p> <p> </p> <p>Furthermore, in the effort of reaching human level performance in terms of accuracy, increasing the complexity and size of ANNs is crucial. Efficient algorithms for evaluating neural network performance is of significant importance to improve the scalability of networks, in addition to designing hardware accelerators. We propose a scalable approach for evaluating Liquid State Machines: a bio-inspired computing model where the inputs are sparsely connected to a randomly interlinked reservoir (or liquid). It has been shown that biological neurons are more likely to be connected to other neurons in the close proximity, and tend to be disconnected as the neurons are spatially far apart. Inspired by this, we propose a group of locally connected neuron reservoirs, or an ensemble of liquids approach, for LSMs. We analyze how the segmentation of a single large liquid to create an ensemble of multiple smaller liquids affects the latency and accuracy of an LSM. In our analysis, we quantify the ability of the proposed ensemble approach to provide an improved representation of the input using the Separation Property (SP) and Approximation Property (AP). Our results illustrate that the ensemble approach enhances class discrimination (quantified as the ratio between the SP and AP), leading to improved accuracy in speech and image recognition tasks, when compared to a single large liquid. Furthermore, we obtain performance benefits in terms of improved inference time and reduced memory requirements, due to lower number of connections and the freedom to parallelize the liquid evaluation process.</p>

<div> <div> <p>Phase transition materials (PTM) have garnered immense interest in concurrent post-CMOS electronics, due to their unique properties such as - electrically driven abrupt resistance switching, hysteresis, and high selectivity. The phase transitions can be attributed to diverse material-specific phenomena, including- correlated electrons, filamentary ion diffusion, and dimerization. In this research, we explore the application space for these materials through extensive device-circuit co-design and propose new ideas harnessing their unique electrical properties. The abrupt transitions and high selectivity of PTMs enable steep (< 60 mV/decade) switching characteristics in Hyper-FET, a promising post-CMOS transistor. We explore device-circuit co-design methodology for Hyper-FET and identify the criterion for material down-selection. We evaluate the achievable voltage swing, energy-delay trade-off, and noise response for this novel device. In addition to the application in low power logic device, PTMs can actively facilitate non-volatile memory design. We propose a PTM augmented Spin Transfer Torque (STT) MRAM that utilizes selective phase transitions to boost the sense margin and stability of stored data, simultaneously. We show that such selective transitions can also be used to improve other MRAM designs with separate read/write paths, avoiding the possibility of read-write conflicts. Further, we analyze the application of PTMs as selectors in cross-point memories. We establish a general simulation framework for cross-point memory array with PTM based <i>selector</i>. We explore the biasing constraints, develop detailed design methodology, and deduce figures of merit for PTM selectors. We also develop a computationally efficient compact model to estimate the leakage through the sneak paths in a cross-point array. Subsequently, we present a new sense amplifier design utilizing PTM, which offers built-in tunable reference with low power and area demand. Finally, we show that the hysteretic characteristics of unipolar PTMs can be utilized to achieve highly efficient rectification. We validate the idea by demonstrating significant design improvements in a <i>Cockcroft-Walton Multiplier, </i>implemented with TS based rectifiers. We emphasize the need to explore other PTMs with high endurance, thermal stability, and faster switching to enable many more innovative applications in the future.</p></div></div>