Power Optimizations in MTJ-based Neural Networks through Stochastic Computing

Artificial Neural Networks (ANNs) have found widespread applications in tasks such as pattern recognition and image classification. However, hardware implementations of ANNs using conventional binary arithmetic units are computationally expensive, energy-intensive and have large area overheads. Stochastic Computing (SC) is an emerging paradigm which replaces these conventional units with simple logic circuits and is particularly suitable for fault-tolerant applications. Spintronic devices, such as Magnetic Tunnel Junctions (MTJs), are capable of replacing CMOS in memory and logic circuits. In this work, we propose an energy-efficient use of MTJs, which exhibit probabilistic switching behavior, as Stochastic Number Generators (SNGs), which forms the basis of our NN implementation in the SC domain. Further, error resilient target applications of NNs allow us to introduce Approximate Computing, a framework wherein accuracy of computations is traded-off for substantial reductions in power consumption. We propose approximating the synaptic weights in our MTJ-based NN implementation, in ways brought about by properties of our MTJ-SNG, to achieve energy-efficiency. We design an algorithm that can perform such approximations within a given error tolerance in a single-layer NN in an optimal way owing to the convexity of the problem formulation. We then use this algorithm and develop a heuristic approach for approximating multi-layer NNs. To give a perspective of the effectiveness of our approach, a 43% reduction in power consumption was obtained with less than 1% accuracy loss on a standard classification problem, with 26% being brought about by the proposed algorithm.Comment: Accepted in the 2017 IEEE/ACM International Conference on Low Power Electronics and Desig

Operation, Monitoring, and Protection of Future Power Systems: Advanced Congestion Forecast and Dynamic State Estimation Applications

The electrical power systems are undergoing drastic changes such as increasing levels of renewable energy sources, energy storage, electrification of energy-efficient loads such as heat pumps and electric vehicles, demand-side resources, etc., in the last decade, and more changes will be followed in the near future. The emergence of digitalization and advanced communication in the case of distribution systems to enhance the performance of the electricity infrastructure also adds further complexities. These changes pose challenges such as increased levels of network congestion, voltage variations, protection mis-operations, increased needs for real-time monitoring, and improved planning practices of the system operators. These challenges will require the development of new paradigms to operate the power grids securely, safely, and economically. This thesis attempted to address those challenges and had the following main contributions:First, the thesis started by presenting a comprehensive assessment framework to address the distribution system operators’ future-readiness and help the distribution system operators to determine the current status of their network infrastructures, business models, and policies and thus identify the pathways for the required developments for the smooth transition towards future intelligent distribution grids.Second, the thesis presents an advanced congestion forecast tool that would support the distribution system operators to forecast and visualize network congestion and voltage variations issues for multiple forecasting horizons ranging from close-to-real time to a day-ahead. The tool is based on a probabilistic power flow that incorporates forecasts of solar photovoltaic production and electricity demand, combined with advanced load models and different operating modes of solar photovoltaic inverters. The tool has been integrated to an existing industrial graded distribution management system via an IoT platform Codex Smart Edge of Atos Worldgrid. The results from case studies demonstrated that the tool performs satisfactorily for both small and large networks and can visualise the cumulative probabilities of network congestion and voltage variations for a variety of forecast horizons as desired by the distribution system operator.Third, a dynamic state estimation-based protection scheme for the transmission lines which does not require complicated relay settings and coordination has been demonstrated using an experimental setup at Chalmers power system laboratory. The scheme makes use of the real-time measurements provided by advanced sensors which are developed by Smart State Technology, The Netherlands. The experimental validations of the scheme have been performed under different fault types and conditions, e.g., unbalanced faults, three-phase faults, high impedance faults, hidden failures, inductive load conditions, etc. The results have shown that the scheme performs adequately in both normal and fault conditions and thus the scheme would work for transmission line protection by avoiding relay coordination and settings issues.Finally, the thesis presents a decentralized dynamic state estimation method for estimating the dynamic states of a transmission line in real-time. This method utilizes the sampled measurements from the local end of a transmission line, and thereafter dynamic state estimation is performed by employing an unscented Kalman filter. The advantage of the method is that the remote end state variables of a transmission line can be estimated using only the local end variables and, hence, the need for communication infrastructure is eliminated. Furthermore, an exact nonlinear model of the transmission line is utilized and the dynamic state estimation of one transmission line is independent of the other lines. These features in turn result in reduced complexity, higher accuracy, and easier implementation of the decentralized estimator. The method is envisioned to have potential applications in transmission line monitoring, control, and protection

Spintronics-based Reconfigurable Ising Model Architecture

Published in the International Symposium On Quality Electronic Design (ISQED), March 2020The Ising model has been explored as a framework for modeling NP-hard problems, with several diverse systems proposed to solve it. The Magnetic Tunnel Junction (MTJ)-based Magnetic RAM is capable of replacing CMOS in memory chips. In this paper, we propose the use of MTJs for representing the units of an Ising model and leveraging its intrinsic physics for finding the ground state of the system through annealing. We design the structure of a basic MTJ-based Ising cell capable of performing the functions essential to an Ising solver. A technique to use the basic Ising cell for scaling to large problems is described. We then go on to propose Ising-FPGA, a parallel and reconfigurable architecture that can be used to map a large class of NP-hard problems, and show how a standard Place and Route tool can be utilized to program the Ising-FPGA. The effects of this hardware platform on our proposed design are characterized and methods to overcome these effects are prescribed. We discuss how two representative NP-hard problems can be mapped to the Ising model. Simulation results show the effectiveness of MTJs as Ising units by producing solutions close/comparable to the optimum, and demonstrate that our design methodology holds the capability to account for the effects of the hardware.This work was supported by the National Science Foundation(NSF) under Grant 164242

Simultaneous Vt selection and assignment for leakage optimization

This paper presents a novel approach for leakage optimiza-tion through simultanous Vt selection and assignment. Vt selection implies deciding the right value for Vt and assign-ment implies deciding which gates should be assigned which thresh-hold value. The proposed algorithm is a general mathematical formulation that can be trivially extended to multiple thresh-hold voltages (more than two). Traditional leakage optimization strategies either assume the prespecifi-cation of thresh-hold values or are good only for two thresh-holds. The presented formulation is based on linear pro-gramming approach under the piecewise linear approxima-tion of delay/leakage vs thresh-hold curves. The algortihm was incorporated in SIS. Experimental results indicate that on some benchmarks having more that two thresh-holds was beneficial for leakage