Multi-Attribute Tradespace Exploration as Front End for Effective Space System Design

The inability to approach systematically the high level of ambiguity present in the early design phases of space systems causes long, highly iterative, and costly design cycles. A process is introduced and described to capture decision maker preferences and use them to generate and evaluate a multitude of space system designs, while providing a common metric that can be easily communicated throughout the design enterprise. Communication channeled through formal utility interviews and analysis enables engineers to better understand the key drivers for the system and allows for a more thorough exploration of the design tradespace. Multi-attribute tradespace exploration with concurrent design, a process incorporating decision theory into model- and simulation-based design, has been applied to several space system projects at the Massachusetts Institute of Technology. Preliminary results indicate that this process can improve the quality of communication to resolve more quickly project ambiguity and to enable the engineer to discover better value designs for multiple stakeholders. The process is also integrated into a concurrent design environment to facilitate the transfer of knowledge of important drivers into higher fidelity design phases. Formal utility theory provides a mechanism to bridge the language barrier between experts of different backgrounds and differing needs, for example, scientists, engineers, managers, etc. Multi-attribute tradespace exploration with concurrent design couples decision makers more closely to the design and, most important, maintains their presence between formal reviews

Pretrial Case Management Under the Amended Rules: Too Many Words for a Good Idea

To cope with the increasing complexity of embedded and cyber-physical system design, different system-level design approaches are proposed which start from abstract models and implement them using design flows with high degrees of automation. However, creating models of such systems and also formulating the mathematical problems arising in these design flows are themselves challenging tasks. A promising approach is the composable construction of these models and problems from more basic entities. Unfortunately, it is non-trivial to propose such compositional formulations today because the current practice in the electronic design automation domain tends to be on using imperative languages and frameworks due to legacy and performance-oriented reasons. This thesis addresses the system design complexity by first promoting proper formalisms and frameworks for capturing models and formulating design-space exploration problems for electronic system-level design in a declarative style; and second, propose realizations based on the industrially accepted languages and frameworks which hold the interesting properties such as composability and parallelism. For modeling, ForSyDe, a denotational system-level modeling formalism for heterogeneous embedded systems is chosen, extended with timed domains to make it more appropriate for capturing cyber-physical systems, and mapped on top of the IEEE standard system design language SystemC. The realized modeling framework, called ForSyDe-SystemC, can be used for modeling systems of heterogeneous nature and their composition to form more sophisticated systems and also conducting parallel and distributed simulation for boosting the simulation speed. Another extension to ForSyDe, named wrapper processes, introduces the ability to compose formal ForSyDe models with legacy IP blocks running in external execution environments to perform a heterogeneous co-simulation. In platform-based design flows, the correct and optimal mapping of an application model onto a flexible platform involves solving a hard problem, named design space exploration. This work proposes Tahmuras, a constraint- based framework to construct generic design space exploration problems as the composition of three individual sub-problems: the application, the platform, and the mapping and scheduling problems. In this way, the model of the design space exploration problem in Tahmuras is automatically generated for each combination of application semantics, target platform, and mapping and scheduling policy simply by composing their respective problems. Using constraint programming, problems can be modeled in a declarative style, while they can be solved in a variety of different styles, including imperative solving heuristics commonly used to solve difficult problems. Efficient parallel solvers exists for constraint programming. Den ökande komplexiteten är en stor utmaning för konstruktionen av framtida inbyggda system. För att möta utmaningen utvecklas nu konstruktionsmetoder som har som mål att starta från en abstrakt modell och att generera en implementering genom ett konstruktionsflöde med hög automatiseringsgrad. Dessvärre är dock skapandet av abstrakta systemmodeller och formaliseringen av de relaterade matematiska problemen i sig ett mycket utmanande problem. Konstruktion genom komposition av basenheter är en lovande idé, men tyvärr är det väldigt svårt att introducera metoden i dagens industriella konstruktionsflöden på grund av imperativa programmeringsspråk och ett gammalt arv i form av existerande kodbas och äldre konstruktioner. Avhandlingen adresserar komplexiten inom systemkonstruktion genom att föreslå passande formalismer för att uttrycka modeller i en deklarativ stil och angripa problemet att hitta en passande implementering. Dessutom visar avhandlingen hur dessa formalismer kan realiseras i en form som kan användas i ett industriellt sammanhang utan att förlora formalismens viktiga grundläggande egenskaper som komposition och parallelism. Modelleringen använder och utökar ForSyDe, en konstruktionsmetod för heterogena inbyggda system. Tilläggen består av en modelleringsmodell som kan fånga specifika egenskaper hos heterogena inbyggda system, samt en implementering av ForSyDe i SystemC, ett industriellt modelleringsspråk som är standardiserat av IEEE. Den nya utvecklingsmiljön, ForSyDe-SystemC, kan användas för att modellera inbyggda system, komponera systemmodeller till större system, samt möjliggör genomförandet av parallella och distribuerade simuleringar med medföljande hög simuleringshastighet. Avhandlingen introducerar också “wrapper”-konceptet i ForSyDe som möjliggör integrationen av existerande modeller och system som en del av en formell ForSyDe-modell och deras co-simulering. ForSyDe-SystemC har använts inom EU-projekt av industriella partner för modellering av egna system. Att hitta en korrekt och effektiv implementering av en abstrakt systemmodell är målet inom aktiviteten “design space exploration” (DSE) som är ett svårt problem för parametriserbara och flexibla plattformar. Avhandlingen presenterar två generationer av Tahmuras, som är baserade på villkorsprogrammering och har som mål att konstruera DSE-problemet som en komposition av tre olika delproblem: applikation, plattform, och bindning. Ett integrerat DSE-problem kan sedan automatiskt genereras genom en kombination av dessa delproblem. Olika metoder, från heuristisk till komplett sökning, kan användas inom villkorsprogrammering för att lösa DSE-problemet. För att visa Tahmuras potential har DSE-metoden validerats med hjälp av olika systemapplikationer av skilda tidsegenskaper och olika plattformar. QC 20141117</p

A methodological approach for algorithmic composition systems' parameter spaces aesthetic exploration

Algorithmic composition is the process of creating musical material by means of formal methods. As a consequence of its design, algorithmic composition systems are (explicitly or implicitly) described in terms of parameters. Thus, parameter space exploration plays a key role in learning the system's capabilities. However, in the computer music field, this task has received little attention. This is due in part, because the produced changes on the human perception of the outputs, as a response to changes on the parameters, could be highly nonlinear, therefore models with strongly predictable outputs are needed. The present work describes a methodology for the human perceptual (or aesthetic) exploration of generative systems' parameter spaces. As the systems' outputs are intended to produce an aesthetic experience on humans, audition plays a central role in the process. The methodology starts from a set of parameter combinations which are perceptually evaluated by the user. The sampling process of such combinations depends on the system under study and possible on heuristic considerations. The evaluated set is processed by a compaction algorithm able to generate linguistic rules describing the distinct perceptions (classes) of the user evaluation. The semantic level of the extracted rules allows for interpretability, while showing great potential in describing high and low-level musical entities. As the resulting rules represent discrete points in the parameter space, further possible extensions for interpolation between points are also discussed. Finally, some practical implementations and paths for further research are presented.Peer ReviewedPostprint (author's final draft

Symbolic Exploration of Transition Hierarchies

In formal design verification, successful model checking is typically preceded by a laborious manual process of constructing design abstractions. We present a methodology for partially - and in some cases, fully - bypassing the abstraction process. For this purpose, we provide to the designer abstraction operators which, if used judiciously in the description of a design, structure the corresponding state space hierarchically. This structure can then be exploited by verification tools, and makes possible the automatic and exhaustive exploration of state spaces that would otherwise be out of scope for existing model checkers. Specifically, we present the following contributions: - A temporal abstraction operator that aggregates transitions and hides intermediate steps. Mathematically, our abstraction operator is a function that maps a flat transition system into a two-level hierarchy where each atomic upper-level transition expands into an entire lower-level transition system. For example, an arithmetic operation may expand to a sequence of bit operations. - A BDD-based algorithm for the symbolic exploration of multi-level hierarchies of transition systems. The algorithm traverses a level-n transition by expanding the corresponding level-(n-1) transition system on-the-fly. The level-n successors of a state are determined by computing a level-(n-1) reach set, which is then immediately released from memory. In this fashion, we can exhaustively explore hierarchically structured state spaces whose flat counterparts cause memory overflows. - We experimentally demonstrate the efficiency of our method with three examples - a multiplier, a cache coherence protocol, and a multiprocessor system. In the first two examples, we obtain significant improvements in run times and peak BDD sizes over traditional state-space search. The third example cannot be model checked at all using conventional methods (without manual abstractions), but can be analyzed fully automatically using transition hierarchies

Formale Methoden zur Systemperformanzanalyse und -optimierung

With increasing system complexity, there is growing interest in using formal methods in wider range of systems to improve system predictability and determine system robustness to changes, enhancements and pitfalls. This paper gives an overview over a formal approach to system level performance modelling and analysis. A methodology is presented to cover distributed multiprocessor systems as well as multiprocessor systems on chip. The abstract modelling allows early design space exploration and optimization. We investigate an example multimedia application and optimize the usage of the shared memory to reach an optimal performance

Semantic programming model-based design

For a generic flexible efficient array antenna receiver platform a hierarchical tiled architecture has been proposed, giving a heterogeneous multi-processor system-on-chip (MPSoC), multiple chips on a board (MCoB) and multiple boards in a system (MBiS). A wide range of MPSoCs are predicted to be used in the near future but how to efficiently apply these designs remains an issue. We will advocate a model-based design approach and propose a single semantic (programming) model for representing the specification, design and implementation and allowing for verification, simulation, architecture definition and design space exploration.\ud \ud A single model for specification, (formal or functional) verification, simulation and programming an MPSoC has obvious as well as some less obvious advantages. It allows for model-based design down to the implementation, especially for hierarchical MPSoC architectures. Partitioning and mapping of the functionality to an architecture is commonly done manually. Using the proposed approach the feasibility of (partly) automated design space exploration is discussed for determining either a partitioning and mapping for a given architecture or an optimal architecture based on set constraints.\ud \ud The proposed hierarchical tiled architecture provides a flexible reconfigurable solution, however partitioning, mapping, modeling and programming such systems remains an issue. The proposed approach tackles these problems at a higher conceptual level, thereby exploiting the inherent composability and parallelism available in the formalism. Design space explorations is facilitated by allowing transformations between different partitionings and mappings. However, the generic applicability and limitations of this approach will need to be researched further.\ud \u

DESIGN SPACE EXPLORATION FOR SIGNAL PROCESSING SYSTEMS USING LIGHTWEIGHT DATAFLOW GRAPHS

Digital signal processing (DSP) is widely used in many types of devices, including mobile phones, tablets, personal computers, and numerous forms of embedded systems. Implementation of modern DSP applications is very challenging in part due to the complex design spaces that are involved. These design spaces involve many kinds of configurable parameters associated with the signal processing algorithms that are used, as well as different ways of mapping the algorithms onto the targeted platforms. In this thesis, we develop new algorithms, software tools and design methodologies to systematically explore the complex design spaces that are involved in design and implementation of signal processing systems. To improve the efficiency of design space exploration, we develop and apply compact system level models, which are carefully formulated to concisely capture key properties of signal processing algorithms, target platforms, and algorithm-platform interactions. Throughout the thesis, we develop design methodologies and tools for integrating new compact system level models and design space exploration methods with lightweight dataflow (LWDF) techniques for design and implementation of signal processing systems. LWDF is a previously-introduced approach for integrating new forms of design space exploration and system-level optimization into design processes for DSP systems. LWDF provides a compact set of retargetable application programming interfaces (APIs) that facilitates the integration of dataflow-based models and methods. Dataflow provides an important formal foundation for advanced DSP system design, and the flexible support for dataflow in LWDF facilitates experimentation with and application of novel design methods that are founded in dataflow concepts. Our developed methodologies apply LWDF programming to facilitate their application to different types of platforms and their efficient integration with platform-based tools for hardware/software implementation. Additionally, we introduce novel extensions to LWDF to improve its utility for digital hardware design and adaptive signal processing implementation. To address the aforementioned challenges of design space exploration and system optimization, we present a systematic multiobjective optimization framework for dataflow-based architectures. This framework builds on the methodology of multiobjective evolutionary algorithms and derives key system parameters subject to time-varying and multidimensional constraints on system performance. We demonstrate the framework by applying LWDF techniques to develop a dataflow-based architecture that can be dynamically reconfigured to realize strategic configurations in the underlying parameter space based on changing operational requirements. Secondly, we apply Markov decision processes (MDPs) for design space exploration in adaptive embedded signal processing systems. We propose a framework, known as the Hierarchical MDP framework for Compact System-level Modeling (HMCSM), which embraces MDPs to enable autonomous adaptation of embedded signal processing under multidimensional constraints and optimization objectives. The framework integrates automated, MDP-based generation of optimal reconfiguration policies, dataflow-based application modeling, and implementation of embedded control software that carries out the generated reconfiguration policies. Third, we present a new methodology for design and implementation of signal processing systems that are targeted to system-on-chip (SoC) platforms. The methodology is centered on the use of LWDF concepts and methods for applying principles of dataflow design at different layers of abstraction. The development processes integrated in our approach are software implementation, hardware implementation, hardware-software co-design, and optimized application mapping. The proposed methodology facilitates development and integration of signal processing hardware and software modules that involve heterogeneous programming languages and platforms. Through three case studies involving complex applications, we demonstrate the effectiveness of the proposed contributions for compact system level design and design space exploration: a digital predistortion (DPD) system, a reconfigurable channelizer for wireless communication, and a deep neural network (DNN) for vehicle classification

Assessment of the Orion-SLS Interface Management Process in Achieving the EIA 731.1 Systems Engineering Capability Model Generic Practices Level 3 Criteria

NASA is currently developing the next generation crewed spacecraft and launch vehicle for exploration beyond earth orbit including returning to the Moon and making the transit to Mars. Managing the design integration of major hardware elements of a space transportation system is critical for overcoming both the technical and programmatic challenges in taking a complex system from concept to space operations. An established method of accomplishing this is formal interface management. In this paper we set forth an argument that the interface management process implemented by NASA between the Orion Multi-Purpose Crew Vehicle (MPCV) and the Space Launch System (SLS) achieves the Level 3 tier of the EIA 731.1 System Engineering Capability Model (SECM) for Generic Practices. We describe the relevant NASA systems and associated organizations, and define the EIA SECM Level 3 Generic Practices. We then provide evidence for our compliance with those practices. This evidence includes discussions of: NASA Systems Engineering Interface (SE) Management standard process and best practices; the tailoring of that process for implementation on the Orion to SLS interface; changes made over time to improve the tailored process, and; the opportunities to take the resulting lessons learned and propose improvements to our institutional processes and best practices. We compare this evidence against the practices to form the rationale for the declared SECM maturity level

Developing Experimental Models for NASA Missions with ASSL

NASA's new age of space exploration augurs great promise for deep space exploration missions whereby spacecraft should be independent, autonomous, and smart. Nowadays NASA increasingly relies on the concepts of autonomic computing, exploiting these to increase the survivability of remote missions, particularly when human tending is not feasible. Autonomic computing has been recognized as a promising approach to the development of self-managing spacecraft systems that employ onboard intelligence and rely less on control links. The Autonomic System Specification Language (ASSL) is a framework for formally specifying and generating autonomic systems. As part of long-term research targeted at the development of models for space exploration missions that rely on principles of autonomic computing, we have employed ASSL to develop formal models and generate functional prototypes for NASA missions. This helps to validate features and perform experiments through simulation. Here, we discuss our work on developing such missions with ASSL.Comment: 7 pages, 4 figures, Workshop on Formal Methods for Aerospace (FMA'09

An integrated modeling framework for infrastructure system-of-systems simulation

Design of future hard infrastructure must consider emergent behaviors from cross-system interdependencies. Understanding these interdependencies is challenging due to high levels of integration in high-performance systems and their operation as a collaborative system-of-systems managed by multiple organizations. Existing modeling frameworks have limitations for strategic planning either because important spatial structure attributes have been abstracted out or behavioral models are oriented to shorter-term analysis with a static network structure. This paper presents a formal modeling framework as a first step to integrating infrastructure system models in a system-of-systems simulation addressing these concerns. First, a graph-theoretic structural framework captures the spatial dimension of physical infrastructure. An element's simulation state includes location, parent, resource contents, and operational state properties. Second, a functional behavioral framework captures the temporal dimension of infrastructure operations at a level suitable for strategic analysis. Resource behaviors determine the flow of resources into or out of nodes and element behaviors modify other state including the network structure. Two application use cases illustrate the usefulness of the modeling framework in varying contexts. The first case applies the framework to future space exploration infrastructure with an emphasis on mobile system elements and discrete resource flows. The second case applies the framework to infrastructure investment in Saudi Arabia with an emphasis on immobile system elements aggregated at the city level and continuous resource flows. Finally, conclusions present future work planned for implementing the framework in a simulation software tool.American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshi