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

ANALYSIS AND OPTIMIZATION OF ELECTRICAL SYSTEMS IN A SOLAR CAR WITH APPLICATIONS TO GATO DEL SOL III-IV

Gato del Sol III, was powered by a solar array of 480 Silicon mono-crystalline photovoltaic cells. Maximum Power Point trackers efficiently made use of these cells and tracked the optimal load. The cells were mounted on a fiber glass and foam core composite shell. The shell rides on a lightweight aluminum space frame chassis, which is powered by a 95% efficient brushless DC motor. Gato del Sol IV was the University of Kentucky Solar Car Team’s (UKSCT) entry into the American Solar Car Challenge (ASC) 2010 event. The car makes use of 310 high density lithium-polymer batteries to account for a 5 kWh pack, enough to travel over 75 miles at 40 mph without power generated by the array. An in-house battery protection system and charge balancing system ensure safe and efficient use of the batteries. Various electrical sub-systems on the car communicate among each other via Controller Area Network (CAN). This real time data is then transmitted to an external computer via RF communication for data collection

An Effective Verification Solution for Modern Microprocessors.

Over the past four decades microprocessors have come to be a vital and inseparable part of the modern world, becoming the digital brain of numerous electronic devices and gadgets that make today's lifestyle possible. Processors are capable of performing computation at astonishingly high speeds and are extremely integrated, occupying only a few square centimeters of silicon die. However, this computational power comes at a price: the task of verifying a modern microprocessor and guaranteeing correctness of its operation is increasingly challenging, even for most established processor vendors. Always attempting to deliver higher performance to end-users, processor manufacturers are forced to design progressively more complex circuits and employ immense verification teams to eliminate critical design bugs in a timely manner. Unfortunately, too often size doesn't seem to matter in verification, as schedules continue to slip and microprocessors find their way to the marketplace with design errors. This work describes a novel verification framework targeting specifically today's complex microprocessors. The scope of the work spans many levels of verification and different phases of the processor life-cycle, from validation of individual sub-modules to complete multi-core system, and from pre-silicon design verification to in-the-field hardware patching. In particular, our StressTest and MCjammer approaches enable efficient generation of high-quality tests at the pre-silicon level for individual cores and multi-core systems, respectively, using machine learning techniques and making the process as automatic as possible. On the other hand, Reversi and Dacota enable low cost validation in post-silicon, while delivering even higher coverage than pre-silicon techniques. Finally, the Field-repairable control logic (FRCL) and Caspar techniques allow designers to patch different classes of escaped errors in processors that are deployed in the field. The integrated set of solutions that we introduce with this thesis empowers processor vendors to drastically shorten their development timeline and, at the same time, to deliver more reliable and correct systems to their customers at a lower cost. Altogether, this work has the potential to solve the long-standing challenge of guaranteeing the complete functional correctness of modern microprocessors.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61656/1/ivagner_1.pd

Testing Multi-Subroutine Quantum Programs: From Unit Testing to Integration Testing

Quantum computing has emerged as a promising field with the potential to revolutionize various domains by harnessing the principles of quantum mechanics. As quantum hardware and algorithms continue to advance, the development of high-quality quantum software has become crucial. However, testing quantum programs poses unique challenges due to the distinctive characteristics of quantum systems and the complexity of multi-subroutine programs. In this paper, we address the specific testing requirements of multi-subroutine quantum programs. We begin by investigating critical properties through a survey of existing quantum libraries, providing insights into the challenges associated with testing these programs. Building upon this understanding, we present a systematic testing process tailored to the intricacies of quantum programming. The process covers unit testing and integration testing, with a focus on aspects such as IO analysis, quantum relation checking, structural testing, behavior testing, and test case generation. We also introduce novel testing principles and criteria to guide the testing process. To evaluate our proposed approach, we conduct comprehensive testing on typical quantum subroutines, including diverse mutations and randomized inputs. The analysis of failures provides valuable insights into the effectiveness of our testing methodology. Additionally, we present case studies on representative multi-subroutine quantum programs, demonstrating the practical application and effectiveness of our proposed testing processes, principles, and criteria.Comment: 53 page

Complaint Driven Training Data Debugging for Machine Learning Workflows

As the need for machine learning (ML) increases rapidly across all industry sectors, so has theinterest in building ML platforms that manage and automate parts of the ML life-cycle. This has enabled companies to use ML inference as a part of their downstream analytics or their applications. Unfortunately, debugging unexpected outcomes in the result of these ML workflows remains a necessary but difficult task of the ML life-cycle. The challenge of debugging ML workflows is that it requires reasoning about the correctness of the workflow logic, the datasets used for inference and training, the models, and interactions between them. Even if the workflow logic is correct, errors in the data used across the ML workflow can still lead to wrong outcomes. In short, developers are not just debugging the code, but also the data. We advocate in favor of a complaint driven approach towards specifying and debugging data errors in ML workflows. The approach takes as input user specified complaints specified as constraints over the final or intermediate outputs of workflows that use trained ML models. The approach outputs explanations in the form of specific operator(s) or data subsets, and how they may be changed to address the constraint violations. In this thesis we make the first steps towards our complaint driven approach to data debugging. As a stepping stone, we focus our attention on complaints specified on top of relational workflows that use ML model inference and whose errors are caused by errors in ML model’s training data. To the best of our knowledge, we contribute the first debugging system for this task, which we call Rain. In response to a user complaint, Rain ranks the ML model’s training examples based on their ability to address the user’s complaint if they were removed. Our experiments show that users can use Rain to debug training data errors by specifying complaints over aggregations of model predictions without having to specify the correct label for each individual prediction. Unfortunately, Rain’s latency may be prohibitive for use in interactive applications like analytical dashboards or business intelligence tools where users are likely to observe errors and complain. To address Rain’s latency problem when scaling to large ML models and training sets, we propose Rain++. Rain++ pushes the majority of Rain’s computation offline ahead of user interaction, achieving orders of magnitude online latency improvements compared to Rain. To go beyond Rain’s and Rain++’s approach that evaluates individual training example deletionsindependently we propose MetaRain, a framework for training classifiers that detect training data corruptions in response to user complaints. Thanks to the generality of MetaRain, users can adapt the classifiers chosen to the training corruptions and the complaints they seek to resolve. Our experiments indicate that making use of this ability results in improved debugging outcomes. Last but not least, we study the problem of updating relational workflow results in response tochanges to the inference ML model used. This can be leveraged by current or future complaint driven debugging systems that repeatedly change the model and reevaluate the relational workflow. We propose FaDE, a compiler that generates efficient code for the workflow update problem by casting it as view maintenance under input tuple deletions. Our experiments indicate that the code generated by FaDE has orders of magnitude lower latency than existing view maintenance systems