MAGNETO-ELECTRIC APPROXIMATE COMPUTATIONAL FRAMEWORK FOR BAYESIAN INFERENCE

Probabilistic graphical models like Bayesian Networks (BNs) are powerful artificial-intelligence formalisms, with similarities to cognition and higher order reasoning in the human brain. These models have been, to great success, applied to several challenging real-world applications. Use of these formalisms to a greater set of applications is impeded by the limitations of the currently used software-based implementations. New emerging-technology based circuit paradigms which leverage physical equivalence, i.e., operating directly on probabilities vs. introducing layers of abstraction, promise orders of magnitude increase in performance and efficiency of BN implementations, enabling networks with millions of random variables. While majority of applications with small network size (100s of nodes) require only single digit precision for accurate results, applications with larger size (1000s to millions of nodes) require higher precision computation. We introduce a new BN integrated circuit fabric based on mixed-signal magneto-electric circuits which perform probabilistic computations based on the principle of approximate computation. Precision scaling in this fabric is logarithmic in area vs. linear in prior directions. Results show 33x area benefit for a 0.001 precision compared to prior direction, while maintaining three orders of magnitude performance benefits vs. 100-core processor implementations

Physically Equivalent Intelligent Systems for Reasoning Under Uncertainty at Nanoscale

Machines today lack the inherent ability to reason and make decisions, or operate in the presence of uncertainty. Machine-learning methods such as Bayesian Networks (BNs) are widely acknowledged for their ability to uncover relationships and generate causal models for complex interactions. However, their massive computational requirement, when implemented on conventional computers, hinders their usefulness in many critical problem areas e.g., genetic basis of diseases, macro finance, text classification, environment monitoring, etc. We propose a new non-von Neumann technology framework purposefully architected across all layers for solving these problems efficiently through physical equivalence, enabled by emerging nanotechnology. The architecture builds on a probabilistic information representation and multi-domain mixed-signal circuit style, and is tightly coupled to a nanoscale physical layer that spans magnetic and electrical domains. Based on bottom-up device-circuit-architecture simulations, we show up to four orders of magnitude performance improvement (using computational resolution of 0.1) vs. best-of-breed multi-core machines with 100 processors, for BNs with about a million variables. Smaller problem sizes of ~100 variables can be realized at 20 mW power consumption and very low area around a few tenths of a mm2. Our vision is to enable solving complex Bayesian problems in real time, as well as enable intelligence capabilities at a small scale everywhere, ushering in a new era of machine intelligence

Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals

Reconstruction of the tridimensional geometry of a visual scene using the binocular disparity information is an important issue in computer vision and mobile robotics, which can be formulated as a Bayesian inference problem. However, computation of the full disparity distribution with an advanced Bayesian model is usually an intractable problem, and proves computationally challenging even with a simple model. In this paper, we show how probabilistic hardware using distributed memory and alternate representation of data as stochastic bitstreams can solve that problem with high performance and energy efficiency. We put forward a way to express discrete probability distributions using stochastic data representations and perform Bayesian fusion using those representations, and show how that approach can be applied to diparity computation. We evaluate the system using a simulated stochastic implementation and discuss possible hardware implementations of such architectures and their potential for sensorimotor processing and robotics.Comment: Preprint of article submitted for publication in International Journal of Approximate Reasoning and accepted pending minor revision

Microstructure design of magneto-dielectric materials via topology optimization

Engineered materials, such as new composites, electromagnetic bandgap and periodic structures have attracted considerable interest in recent years due to their remarkable and unique electromagnetic behavior. As a result, an extensive literature on the theory and application of artificially modified materials exists. Examples include photonic crystals (regular, degenerate or magnetic) illustrating that extraordinary gain and high transmittance can be achieved at specific frequencies. Of importance is that recent investigations of material loading demonstrate that substantial improvements in antenna performance (smaller size, larger bandwidth, higher gain etc.) can be attained by loading bulk materials such as ferrites or by simply grading the material subject to specific design objectives. Multi-tone ceramic materials have also been used for miniaturization and pliable polymers offer new possibilities in three dimensional antenna design and multilayer printed structures, including 3D electronics. However, as the variety of examples in the literature shows, the perfect combination of materials is unique and extremely difficult to determine without optimization. In addition, existing artificial dielectrics are mostly based on intuitive studies, i.e. a formal design framework to predict the exact spatial combination of dielectrics, magnetics and conductors does not exist. In the first part of this thesis, an inverse design framework integrating FE based analysis tool (COMSOL MULTIPHYSICS-PDE Coefficient Module) with an optimization technique (MATLAB-Genetic Algorithm and Direct Search toolbox) suitable for designing the microstructure of artificial magneto-dielectrics from isotropic material phases is proposed. Homogenizing Maxwell's Equations (MEQ) in order to estimate the effective material parameters of the desired composite made of periodic microstructures is the initial task of the framework. The FE analysis tool is used to evaluate intermediate fields at the "micro-scale" level of a unit cell that is integrated with the homogenized MEQ's in order to estimate the "macro-scale" effective constitutive parameters of the overall bulk periodic structure. Simulation of the periodic structure is an extremely challenging task due to the mesh at micro-level (inclusions much smaller than the periodic cell dimension) that spans over the entire bulk structure turning the computational problem into a very intensive one. Therefore, the proposed framework based on the solution of homogenized MEQ's via the micro-macro approach, allows topology design capabilities of microstructures with desired properties. The goal is to achieve predefined material constitutive parameters via artificial electromagnetic substrates. Physical material bounds on the attainable properties are studied to avoid infeasible effective parameter requirements via available multi-constituents. The proposed framework is applied on examples such as microstructure layers of non-reciprocal magnetic photonic crystals. Results show that the homogenization technique along with topology optimization is able to design non-intuitive material compositions with desired electromagnetic properties. In the second part of the thesis, approximation techniques to speed-up large scale topology optimization studies of devices with complex frequency responses are investigated. Miniaturization of microstrip antennas via topology optimization of both the conductor and material substrate via multi-tone ceramic shades is a typical example treated here. Long computational times required for both the electromagnetic analysis over a frequency range and the need for a heuristic based optimization tool to locate the global minima for complex devices present themselves as two important bottlenecks for practical design studies. In this thesis, two new techniques for speeding up the optimization process by reducing the number of frequency calls needed to accurately predict a multi-resonance type response of a candidate design are proposed. The proposed techniques employ adaptive sampling methods along with novel rational function interpolations. The first technique relies on a heuristic based rational interpolation using Bayes' theory and rational functions. Second, a rational function interpolation employing a new adaptive path based on Stoer-Bulirsch algorithm is used. Both techniques prove to efficiently predict resonances and significantly reduce the computational time by at least three folds

A Bayesian approach for the stochastic modeling error reduction of magnetic material identification of an electromagnetic device

Magnetic material properties of an electromagnetic device can be recovered by solving an inverse problem where measurements are adequately interpreted by a mathematical forward model. The accuracy of these forward models dramatically affects the accuracy of the material properties recovered by the inverse problem. The more accurate the forward model is, the more accurate recovered data are. However, the more accurate ‘fine’ models demand a high computational time and memory storage. Alternatively, less accurate ‘coarse’ models can be used with a demerit of the high expected recovery errors. This paper uses the Bayesian approximation error approach for improving the inverse problem results when coarse models are utilized. The proposed approach adapts the objective function to be minimized with the a priori misfit between fine and coarse forward model responses. In this paper, two different electromagnetic devices, namely a switched reluctance motor and an EI core inductor, are used as case studies. The proposed methodology is validated on both purely numerical and real experimental results. The results show a significant reduction in the recovery error within an acceptable computational time

Investigating cortico-subcortical circuits during auditory sensory attenuation: A combined magnetoencephalographic and dynamic causal modeling study

Sensory attenuation refers to the decreased intensity of a sensory percept when a sensation is self‐generated compared with when it is externally triggered. However, the underlying brain regions and network interactions that give rise to this phenomenon remain to be determined. To address this issue, we recorded magnetoencephalographic (MEG) data from 35 healthy controls during an auditory task in which pure tones were either elicited through a button press or passively presented. We analyzed the auditory M100 at sensor‐ and source‐level and identified movement‐related magnetic fields (MRMFs). Regression analyses were used to further identify brain regions that contributed significantly to sensory attenuation, followed by a dynamic causal modeling (DCM) approach to explore network interactions between generators. Attenuation of the M100 was pronounced in right Heschl's gyrus (HES), superior temporal cortex (ST), thalamus, rolandic operculum (ROL), precuneus and inferior parietal cortex (IPL). Regression analyses showed that right postcentral gyrus (PoCG) and left precentral gyrus (PreCG) predicted M100 sensory attenuation. In addition, DCM results indicated that auditory sensory attenuation involved bi‐directional information flow between thalamus, IPL, and auditory cortex. In summary, our data show that sensory attenuation is mediated by bottom‐up and top‐down information flow in a thalamocortical network, providing support for the role of predictive processing in sensory‐motor system

BOOLEAN AND BRAIN-INSPIRED COMPUTING USING SPIN-TRANSFER TORQUE DEVICES

Several completely new approaches (such as spintronic, carbon nanotube, graphene, TFETs, etc.) to information processing and data storage technologies are emerging to address the time frame beyond current Complementary Metal-Oxide-Semiconductor (CMOS) roadmap. The high speed magnetization switching of a nano-magnet due to current induced spin-transfer torque (STT) have been demonstrated in recent experiments. Such STT devices can be explored in compact, low power memory and logic design. In order to truly leverage STT devices based computing, researchers require a re-think of circuit, architecture, and computing model, since the STT devices are unlikely to be drop-in replacements for CMOS. The potential of STT devices based computing will be best realized by considering new computing models that are inherently suited to the characteristics of STT devices, and new applications that are enabled by their unique capabilities, thereby attaining performance that CMOS cannot achieve. The goal of this research is to conduct synergistic exploration in architecture, circuit and device levels for Boolean and brain-inspired computing using nanoscale STT devices. Specifically, we first show that the non-volatile STT devices can be used in designing configurable Boolean logic blocks. We propose a spin-memristor threshold logic (SMTL) gate design, where memristive cross-bar array is used to perform current mode summation of binary inputs and the low power current mode spintronic threshold device carries out the energy efficient threshold operation. Next, for brain-inspired computing, we have exploited different spin-transfer torque device structures that can implement the hard-limiting and soft-limiting artificial neuron transfer functions respectively. We apply such STT based neuron (or ‘spin-neuron’) in various neural network architectures, such as hierarchical temporal memory and feed-forward neural network, for performing “human-like” cognitive computing, which show more than two orders of lower energy consumption compared to state of the art CMOS implementation. Finally, we show the dynamics of injection locked Spin Hall Effect Spin-Torque Oscillator (SHE-STO) cluster can be exploited as a robust multi-dimensional distance metric for associative computing, image/ video analysis, etc. Our simulation results show that the proposed system architecture with injection locked SHE-STOs and the associated CMOS interface circuits can be suitable for robust and energy efficient associative computing and pattern matching

Optimal quantum control with poor statistics

Control of quantum systems is a central element of high-precision experiments and the development of quantum technological applications. Control pulses that are typically temporally or spatially modulated are often designed based on theoretical simulations. As we gain control over larger and more complex quantum systems, however, we reach the limitations of our capabilities of theoretical modeling and simulations, and learning how to control a quantum system based exclusively on experimental data can help us to exceed those limitations. Due to the intrinsic probabilistic nature of quantum mechanics, it is fundamentally necessary to repeat measurements on individual quantum systems many times in order to estimate the expectation value of an observable with good accuracy. Control algorithms requiring accurate data can thus imply an experimental effort that negates the benefits of avoiding theoretical modeling. We present a control algorithm based on Bayesian optimization that finds optimal control solutions in the presence of large measurement shot noise and even in the limit of single-shot measurements. With several numerical and experimental examples we demonstrate that this method is capable of finding excellent control solutions with minimal experimental effor