Reversible Computation: Extending Horizons of Computing

This open access State-of-the-Art Survey presents the main recent scientific outcomes in the area of reversible computation, focusing on those that have emerged during COST Action IC1405 "Reversible Computation - Extending Horizons of Computing", a European research network that operated from May 2015 to April 2019. Reversible computation is a new paradigm that extends the traditional forwards-only mode of computation with the ability to execute in reverse, so that computation can run backwards as easily and naturally as forwards. It aims to deliver novel computing devices and software, and to enhance existing systems by equipping them with reversibility. There are many potential applications of reversible computation, including languages and software tools for reliable and recovery-oriented distributed systems and revolutionary reversible logic gates and circuits, but they can only be realized and have lasting effect if conceptual and firm theoretical foundations are established first

Reversible Computation: Extending Horizons of Computing

This open access State-of-the-Art Survey presents the main recent scientific outcomes in the area of reversible computation, focusing on those that have emerged during COST Action IC1405 "Reversible Computation - Extending Horizons of Computing", a European research network that operated from May 2015 to April 2019. Reversible computation is a new paradigm that extends the traditional forwards-only mode of computation with the ability to execute in reverse, so that computation can run backwards as easily and naturally as forwards. It aims to deliver novel computing devices and software, and to enhance existing systems by equipping them with reversibility. There are many potential applications of reversible computation, including languages and software tools for reliable and recovery-oriented distributed systems and revolutionary reversible logic gates and circuits, but they can only be realized and have lasting effect if conceptual and firm theoretical foundations are established first

Hybrid Analog-Digital Co-Processing for Scientific Computation

In the past 10 years computer architecture research has moved to more heterogeneity and less adherence to conventional abstractions. Scientists and engineers hold an unshakable belief that computing holds keys to unlocking humanity's Grand Challenges. Acting on that belief they have looked deeper into computer architecture to find specialized support for their applications. Likewise, computer architects have looked deeper into circuits and devices in search of untapped performance and efficiency. The lines between computer architecture layers---applications, algorithms, architectures, microarchitectures, circuits and devices---have blurred. Against this backdrop, a menagerie of computer architectures are on the horizon, ones that forgo basic assumptions about computer hardware, and require new thinking of how such hardware supports problems and algorithms. This thesis is about revisiting hybrid analog-digital computing in support of diverse modern workloads. Hybrid computing had extensive applications in early computing history, and has been revisited for small-scale applications in embedded systems. But architectural support for using hybrid computing in modern workloads, at scale and with high accuracy solutions, has been lacking. I demonstrate solving a variety of scientific computing problems, including stochastic ODEs, partial differential equations, linear algebra, and nonlinear systems of equations, as case studies in hybrid computing. I solve these problems on a system of multiple prototype analog accelerator chips built by a team at Columbia University. On that team I made contributions toward programming the chips, building the digital interface, and validating the chips' functionality. The analog accelerator chip is intended for use in conjunction with a conventional digital host computer. The appeal and motivation for using an analog accelerator is efficiency and performance, but it comes with limitations in accuracy and problem sizes that we have to work around. The first problem is how to do problems in this unconventional computation model. Scientific computing phrases problems as differential equations and algebraic equations. Differential equations are a continuous view of the world, while algebraic equations are a discrete one. Prior work in analog computing mostly focused on differential equations; algebraic equations played a minor role in prior work in analog computing. The secret to using the analog accelerator to support modern workloads on conventional computers is that these two viewpoints are interchangeable. The algebraic equations that underlie most workloads can be solved as differential equations, and differential equations are naturally solvable in the analog accelerator chip. A hybrid analog-digital computer architecture can focus on solving linear and nonlinear algebra problems to support many workloads. The second problem is how to get accurate solutions using hybrid analog-digital computing. The reason that the analog computation model gives less accurate solutions is it gives up representing numbers as digital binary numbers, and instead uses the full range of analog voltage and current to represent real numbers. Prior work has established that encoding data in analog signals gives an energy efficiency advantage as long as the analog data precision is limited. While the analog accelerator alone may be useful for energy-constrained applications where inputs and outputs are imprecise, we are more interested in using analog in conjunction with digital for precise solutions. This thesis gives novel insight that the trick to do so is to solve nonlinear problems where low-precision guesses are useful for conventional digital algorithms. The third problem is how to solve large problems using hybrid analog-digital computing. The reason the analog computation model can't handle large problems is it gives up step-by-step discrete-time operation, instead allowing variables to evolve smoothly in continuous time. To make that happen the analog accelerator works by chaining hardware for mathematical operations end-to-end. During computation analog data flows through the hardware with no overheads in control logic and memory accesses. The downside is then the needed hardware size grows alongside problem sizes. While scientific computing researchers have for a long time split large problems into smaller subproblems to fit in digital computer constraints, this thesis is a first attempt to consider these divide-and-conquer algorithms as an essential tool in using the analog model of computation. As we enter the post-Moore’s law era of computing, unconventional architectures will offer specialized models of computation that uniquely support specific problem types. Two prominent examples are deep neural networks and quantum computers. Recent trends in computer science research show these unconventional architectures will soon have broad adoption. In this thesis I show another specialized, unconventional architecture is to use analog accelerators to solve problems in scientific computing. Computer architecture researchers will discover other important models of computation in the future. This thesis is an example of the discovery process, implementation, and evaluation of how an unconventional architecture supports specialized workloads

A Variable-Structure Variable-Order Simulation Paradigm for Power Electronic Circuits

Solid-state power converters are used in a rapidly growing number of applications including variable-speed motor drives for hybrid electric vehicles and industrial applications, battery energy storage systems, and for interfacing renewable energy sources and controlling power flow in electric power systems. The desire for higher power densities and improved efficiencies necessitates the accurate prediction of switching transients and losses that, historically, have been categorized as conduction and switching losses. In the vast majority of analyses, the power semiconductors (diodes, transistors) are represented using simplified or empirical models. Conduction losses are calculated as the product of circuit-dependent currents and on-state voltage drops. Switching losses are estimated using approximate voltage-current waveforms with empirically derived turn-on and turn-off times

Numerical solutions of differential equations on FPGA-enhanced computers

Conventionally, to speed up scientific or engineering (S&E) computation programs on general-purpose computers, one may elect to use faster CPUs, more memory, systems with more efficient (though complicated) architecture, better software compilers, or even coding with assembly languages. With the emergence of Field Programmable Gate Array (FPGA) based Reconfigurable Computing (RC) technology, numerical scientists and engineers now have another option using FPGA devices as core components to address their computational problems. The hardware-programmable, low-cost, but powerful “FPGA-enhanced computer” has now become an attractive approach for many S&E applications. A new computer architecture model for FPGA-enhanced computer systems and its detailed hardware implementation are proposed for accelerating the solutions of computationally demanding and data intensive numerical PDE problems. New FPGAoptimized algorithms/methods for rapid executions of representative numerical methods such as Finite Difference Methods (FDM) and Finite Element Methods (FEM) are designed, analyzed, and implemented on it. Linear wave equations based on seismic data processing applications are adopted as the targeting PDE problems to demonstrate the effectiveness of this new computer model. Their sustained computational performances are compared with pure software programs operating on commodity CPUbased general-purpose computers. Quantitative analysis is performed from a hierarchical set of aspects as customized/extraordinary computer arithmetic or function units, compact but flexible system architecture and memory hierarchy, and hardwareoptimized numerical algorithms or methods that may be inappropriate for conventional general-purpose computers. The preferable property of in-system hardware reconfigurability of the new system is emphasized aiming at effectively accelerating the execution of complex multi-stage numerical applications. Methodologies for accelerating the targeting PDE problems as well as other numerical PDE problems, such as heat equations and Laplace equations utilizing programmable hardware resources are concluded, which imply the broad usage of the proposed FPGA-enhanced computers

Lifetime prediction for power converters

Renewable energy is developing rapidly and gaining more and more commercial viability. High reliability of the generation system is essential to maximize the output power. The power inverter is an important unit in this system and is believed to be one of the most unreliable parts. In the case of wind power generation, especially in off-shore wind, when the system reliability requirement is high, a technique to predict the inverter lifetime is invaluable as it would help the inverter designer optimize his design for minimal maintenance. Previous researchers studying inverter lifetime prediction, focus either at device level such as device fatigue damage models, or at system level which require experimental data for their selected device. This work presents a new method to estimate the inverter lifetime from a given mission profile within a reasonable simulation time. Such model can be used as a converter design tool or an on-line lifetime estimation tool after being configured to a real converter system. The key contribution of this work is to link the physics of the power devices to a large scale system simulation within a reasonable framework of time. With this technique, the system down time can be reduced and therefore more power can be generated. Also, the failure damage to the system is avoided which reduces the maintenance cost. A power cycling test is designed to gather the lifetime data of a selected IGBT module. Die-attach solder fatigue is found out to be the dominant failure mode of this IGBT module. The accuracy of widely accepted Miner’s rule, which accumulates damage linearly, is discussed and a nonlinear accumulation method is promoted to predict the lifetime of power inverters

Computationally-efficient Finite-element-based Thermal and Electromagnetic Models of Electric Machines.

With the modern trend of transportation electrification, electric machines are a key component of electric/hybrid electric vehicle (EV/HEV) powertrains. It is therefore important that vehicle powertrain-level and system-level designers and control engineers have access to accurate yet computationally-efficient (CE), physics-based modeling tools of the thermal and electromagnetic (EM) behavior of electric machines. In this dissertation, CE yet accurate thermal and EM models for electric machines, which are suitable for use in vehicle powertrain design, optimization, and control, are developed. This includes not only creating fast and accurate thermal and EM models for specific machine designs, but also the ability to quickly generate and determine the performance of new machine designs through the application of scaling techniques to existing designs. With the developed techniques, the thermal and EM performance can be accurately and efficiently estimated. Furthermore, powertrain or system designers can easily and quickly adjust the characteristics and the performance of the machine in ways that are favorable to the overall vehicle performance.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113497/1/kanzhou_1.pd

Cost modelling and concurrent engineering for testable design

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.As integrated circuits and printed circuit boards increase in complexity, testing becomes a major cost factor of the design and production of the complex devices. Testability has to be considered during the design of complex electronic systems, and automatic test systems have to be used in order to facilitate the test. This fact is now widely accepted in industry. Both design for testability and the usage of automatic test systems aim at reducing the cost of production testing or, sometimes, making it possible at all. Many design for testability methods and test systems are available which can be configured into a production test strategy, in order to achieve high quality of the final product. The designer has to select from the various options for creating a test strategy, by maximising the quality and minimising the total cost for the electronic system. This thesis presents a methodology for test strategy generation which is based on consideration of the economics during the life cycle of the electronic system. This methodology is a concurrent engineering approach which takes into account all effects of a test strategy on the electronic system during its life cycle by evaluating its related cost. This objective methodology is used in an original test strategy planning advisory system, which allows for test strategy planning for VLSI circuits as well as for digital electronic systems. The cost models which are used for evaluating the economics of test strategies are described in detail and the test strategy planning system is presented. A methodology for making decisions which are based on estimated costing data is presented. Results of using the cost models and the test strategy planning system for evaluating the economics of test strategies for selected industrial designs are presented