Digital Signal Processing and Machine Learning System Design using Stochastic Logic

University of Minnesota Ph.D. dissertation. July 2017. Major: Electrical/Computer Engineering. Advisor: Keshab Parhi. 1 computer file (PDF); xxii, 172 pages.Digital signal processing (DSP) and machine learning systems play a crucial role in the fields of big data and artificial intelligence. The hardware design of these systems is extremely critical to meet stringent application requirements such as extremely small size, low power consumption, and high reliability. Following the path of Moore's Law, the density and performance of hardware systems are dramatically improved at an exponential pace. The increase in the number of transistors on a chip, which plays the main role in improvement in the density of circuit design, causes rapid increase in circuit complexity. Therefore, low area consumption is one of the key challenges for IC design, especially for portable devices. Another important challenge for hardware design is reliability. A chip fabricated using nanoscale complementary metal-oxide-semiconductor (CMOS) technologies will be prone to errors caused by fluctuations in threshold voltage, supply voltage, doping levels, aging, timing errors and soft errors. Design of nanoscale failure-resistant systems is currently of significant interest, especially as the technology scales below 10 nm. Stochastic Computing (SC) is a novel approach to address these challenges in system and circuit design. This dissertation considers the design of digital signal processing and machine learning systems in stochastic logic. The stochastic implementations of finite impulse response (FIR) and infinite impulse response (IIR) filters based on various lattice structures are presented. The implementations of complex functions such as trigonometric, exponential, and sigmoid, are derived based on truncated versions of their Maclaurin series expansions. We also present stochastic computation of polynomials using stochastic subtractors and factorization. The machine learning systems including artificial neural network (ANN) and support vector machine (SVM) in stochastic logic are also presented. First, we propose novel implementations for linear-phase FIR filters in stochastic logic. The proposed design is based on lattice structures. Compared to direct-form linear-phase FIR filters, linear-phase lattice filters require twice the number of multipliers but the same number of adders. The hardware complexities of stochastic implementations of linear-phase FIR filters for direct-form and lattice structures are comparable. We propose stochastic implementation of IIR filters using lattice structures where the states are orthogonal and uncorrelated. We present stochastic IIR filters using basic, normalized and modified lattice structures. Simulation results demonstrate high signal-to-error ratio and fault tolerance in these structures. Furthermore, hardware synthesis results show that these filter structures require lower hardware area and power compared to two's complement realizations. Second, We present stochastic logic implementations of complex arithmetic functions based on truncated versions of their Maclaurin series expansions. It is shown that a polynomial can be implemented using multiple levels of NAND gates based on Horner's rule, if the coefficients are alternately positive and negative and their magnitudes are monotonically decreasing. Truncated Maclaurin series expansions of arithmetic functions are used to generate polynomials which satisfy these constraints. The input and output in these functions are represented by unipolar representation. For a polynomial that does not satisfy these constraints, it still can be implemented based on Horner's rule if each factor of the polynomial satisfies these constraints. format conversion is proposed for arithmetic functions with input and output represented in different formats, such as $\text{cos}\,\pi x$ given $x\in[0,1]$ and $\text{sigmoid(x)}$ given $x\in[-1,1]$. Polynomials are transformed to equivalent forms that naturally exploit format conversions. The proposed stochastic logic circuits outperform the well-known Bernstein polynomial based and finite-state-machine (FSM) based implementations. Furthermore, the hardware complexity and the critical path of the proposed implementations are less than the Bernstein polynomial based and FSM based implementations for most cases. Third, we address subtraction and polynomial computations using unipolar stochastic logic. It is shown that stochastic computation of polynomials can be implemented by using a stochastic subtractor and factorization. Two approaches are proposed to compute subtraction in stochastic unipolar representation. In the first approach, the subtraction operation is approximated by cascading multi-levels of OR and AND gates. The accuracy of the approximation is improved with the increase in the number of stages. In the second approach, the stochastic subtraction is implemented using a multiplexer and a stochastic divider. We propose stochastic computation of polynomials using factorization. Stochastic implementations of first-order and second-order factors are presented for different locations of polynomial roots. From experimental results, it is shown that the proposed stochastic logic circuits require less hardware complexity than the previous stochastic polynomial implementation using Bernstein polynomials. Finally, this thesis presents novel architectures for machine learning based classifiers using stochastic logic. Three types of classifiers are considered. These include: linear support vector machine (SVM), artificial neural network (ANN) and radial basis function (RBF) SVM. These architectures are validated using seizure prediction from electroencephalogram (EEG) as an application example. To improve the accuracy of proposed stochastic classifiers, an approach of data-oriented linear transform for input data is proposed for EEG signal classification using linear SVM classifiers. Simulation results in terms of the classification accuracy are presented for the proposed stochastic computing and the traditional binary implementations based datasets from two patients. It is shown that accuracies of the proposed stochastic linear SVM are improved by 3.88\% and 85.49\% for datasets from patient-1 and patient-2, respectively, by using the proposed linear-transform for input data. Compared to conventional binary implementation, the accuracy of the proposed stochastic ANN is improved by 5.89\% for the datasets from patient-1. For patient-2, the accuracy of the proposed stochastic ANN is improved by 7.49\% by using the proposed linear-transform for input data. Additionally, compared to the traditional binary linear SVM and ANN, the hardware complexity, power consumption and critical path of the proposed stochastic implementations are reduced significantly

Physics and Applications of Laser Diode Chaos

An overview of chaos in laser diodes is provided which surveys experimental achievements in the area and explains the theory behind the phenomenon. The fundamental physics underpinning this behaviour and also the opportunities for harnessing laser diode chaos for potential applications are discussed. The availability and ease of operation of laser diodes, in a wide range of configurations, make them a convenient test-bed for exploring basic aspects of nonlinear and chaotic dynamics. It also makes them attractive for practical tasks, such as chaos-based secure communications and random number generation. Avenues for future research and development of chaotic laser diodes are also identified.Comment: Published in Nature Photonic

MOCAST 2021

The 10th International Conference on Modern Circuit and System Technologies on Electronics and Communications (MOCAST 2021) will take place in Thessaloniki, Greece, from July 5th to July 7th, 2021. The MOCAST technical program includes all aspects of circuit and system technologies, from modeling to design, verification, implementation, and application. This Special Issue presents extended versions of top-ranking papers in the conference. The topics of MOCAST include:Analog/RF and mixed signal circuits;Digital circuits and systems design;Nonlinear circuits and systems;Device and circuit modeling;High-performance embedded systems;Systems and applications;Sensors and systems;Machine learning and AI applications;Communication; Network systems;Power management;Imagers, MEMS, medical, and displays;Radiation front ends (nuclear and space application);Education in circuits, systems, and communications

FPGA-Based Degradation and Reliability Monitor for Underground Cables

The online Remaining Useful Life (RUL) estimation of underground cables and their reliability analysis requires obtaining the cable failure time probability distribution. Monte Carlo (MC) simulations of complex thermal heating and electro-thermal degradation models can be employed for this analysis, but uncertainties need to be considered in the simulations, to produce accurate RUL expectation values and confidence margins for the results. The process requires performing large simulation sets, based on past temperature or load measurements and future load predictions. Field Programmable Gate Arrays (FPGAs) permit accelerating simulations for live analysis, but the thermal models involved are complex to be directly implemented in hardware logic. A new standalone FPGA architecture has been proposed for the fast and on-site degradation and reliability analysis of underground cables, based on MC simulation, and the effect of load uncertainties on the predicted cable End Of Life (EOL) has been analyzed from the results

Linear Phase FIR Low Pass Filter Design Based on Firefly Algorithm

In this paper, a linear phase Low Pass FIR filter is designed and proposed based on Firefly algorithm. We exploit the exploitation and exploration mechanism with a local search routine to improve the convergence and get higher speed computation. The optimum FIR filters are designed based on the Firefly method for which the finite word length is used to represent coefficients. Furthermore, Particle Swarm Optimization (PSO) and Differential Evolution algorithm (DE) will be used to show the solution. The results will be compared with PSO and DE methods. Firefly algorithm and Parks–McClellan (PM) algorithm are also compared in this paper thoroughly. The design goal is successfully achieved in all design examples using the Firefly algorithm. They are compared with that obtained by using the PSO and the DE algorithm. For the problem at hand, the simulation results show that the Firefly algorithm outperforms the PSO and DE methods in some of the presented design examples. It also performs well in a portion of the exhibited design examples particularly in speed and quality