-Nowadays many design space exploration tools are based on Multi-Objective Evolutionary Algorithms (MOEAs). Beside the advantages of MOEAs, there is one important drawback as MOEAs might fail in design spaces containing only a few feasible solutions or as they are often afflicted with premature convergence, i.e., the same design points are revisited again and again. Exact methods, especially Pseudo Boolean solvers (PB solvers) seem to be a solution. However, as typical design spaces are multi-objective, there is a need for multi-objective PB solvers. In this paper, we will formalize the problem of design space exploration as multi-objective 0-1 ILP. We will propose (1) a heuristic approach based on PB solvers and (2) a complete multi-objective PB solver based on a backtracking algorithm that incorporates the non-dominance relation from multi-objective optimization and is restricted to linear objective functions. First results from applying our novel multi-objective PB solver to synthetic problems will show its effectiveness in small sized design spaces as well as in large design spaces only containing a few feasible solutions. For non-linear and large problems, the proposed heuristic approach is outperforming common MOEA approaches. Finally, a real world example from the automotive area will emphasize the efficiency of the proposed algorithms.

We demonstrate an approach to violating Bell's inequality with the continuous spatial variables of entangled-photon pairs using simple optical components that manipulate the spatial parity of the transverse coordinate in one dimension.The Einstein-Podolsky-Rosen 1 (EPR) argument revealed the paradoxical properties of a two-particle system entangled continuously in the spatial parameter. A direct test of quantum nonlocality exhibited by this state, via a violation of Bell's inequality 2 , has not been forthcoming. Here we first establish an isomorphism between the singlemode multiphoton electromagnetic-field-space spanned by a Fock-state basis and the single-photon multimode electromagnetic-field-space spanned by a spatial-eigenmode basis. We then construct a Hilbert space with a twodimensional basis of spatial even-odd parity modes. This allows us, as a result, to identify and construct experimental arrangements comprising simple optical components, with no nonlinearities or moving parts, which implement operators in the spatial parity space of single-photon fields which correspond to the familiar Pauli spin operators. A set of such operators are shown in Fig. 1, where we also show a construction of an SO(2) operator that implements a rotation in spatial parity space (Fig. 1d), and a parity analyzer (Fig. 1e) that projects the input state onto the spatial parity basis (i.e., even and odd spatial components of the input field).

Abstract-High computational effort in modern image processing applications like medical imaging or high-resolution video processing often demands for massively parallel special purpose architectures in form of FPGAs or ASICs. However, their efficient implementation is still a challenge, as the design complexity causes exploding development times and costs. This paper presents a new design flow which permits to specify, analyze, and synthesize complex image processing algorithms. A novel buffer requirement analysis allows exploiting possible tradeoffs between required communication memory and computational logic for multi-rate applications. The derived schedule and buffer results are taken into account for resource optimized synthesis of the required hardware accelerators. Application to a multi-resolution filter shows that buffer analysis is possible in less than one second and that scheduling alternatives influence the required communication memory by up to 24% and the computational resources by up to 16%. I. INTRODUCTIONAs design complexity is becoming a major barrier for technical progress because of expensive and error-prone development, new design methodologies raising the level of abstraction are becoming increasingly popular. Simulink [1] or SystemC based high-level synthesis [2] tools for instance permit to compose complex systems by communicating blocks. However, these approaches do not allow for system-level analysis like determination of required communication buffer sizes, as the blocks can contain arbitrarily complex operations. Alternative approaches like [3], [4] are restricted to a subset of sequential languages like C. However, extraction of the contained parallelism is challenging, especially as analysis on individual statements can get computationally expensive [5].In order to address these aspects, this paper presents a novel design flow for high-level synthesis of complex multi-rate image processing applications containing up-and downsamplers. It extends existing previous work by usage of latticebased buffer analysis which considers different scheduling alternatives for multi-rate systems. As the obtained results are directly taken into account during hardware synthesis, we are able to exploit tradeoffs between required communication memory and computational logic. Furthermore, in contrast to many other approaches, analysis of the overall system does not rely on solving Integer Linear Programs (ILPs) in case of acyclic problems. Instead ILPs are only required for local analysis like actor synthesis or dependency calculation in order to assure good scaling properties of our design flow.

In this work, the focus is put on the behavior of a system in case a fault occurs that disables the system from executing its applications. Instead of executing a random subset of the applications depending on the fault, an approach is presented that optimizes the systems structure and behavior with respect to a possible graceful degradation. It includes a degradationaware reliability analysis that guides the optimization of the resource allocation and function distribution, and provides datastructures for an efficient online degradation algorithm. Thus, the proposed methodology covers both, the design phase with a structural optimization and the online phase with a behavioral optimization of the system. A case study shows the effectiveness of the proposed approach.

Abstract-This paper presents a methodology to evaluate and optimize the robustness of an embedded system in terms of invariability in case of design revisions. Early decisions in embedded system design may be revised in later stages resulting in additional costs. A method that quantifies the expected additional costs as the robustness value is proposed. Since the determination of the robustness based on arbitrary revisions is computationally expensive, an efficient set-based approach that uses a symbolic encoding as Binary Decision Diagrams is presented. Moreover, a methodology for the integration of the optimization of the robustness into a design space exploration is proposed. Based on an external archive that accepts also near-optimal solutions, this robustness-aware optimization is efficient since it does not require additional function evaluations as previous approaches. Two realistic case studies give evidence of the benefits of the proposed approach. I. INTRODUCTIONA large number of design decisions are made early in the design process of embedded systems. If one or more of these design decision have to be revised later in the design process, additional costs arise. In a worst-case scenario, a single revised design decision might lead to a string of additional design changes in order to keep the solution feasible in terms of both the design and objectives. A viable approach to avoid these late changes might be the exclusion of all solutions that contain any critical design decision. However, in general this approach is too restrictive, leading either to a lack of feasible designs or unacceptable objectives.In order to overcome these shortcomings in state-of-the-art embedded system design, this paper introduces the robustness related to design revisions. In this context, a solution is termed robust if the expected additional costs due to design revisions are minor. The determined robustness value shall support a designer in the decision making by additional information about the design alternatives. On the other hand, the robustness might be used in a design space exploration to enable the search for robust solutions.Contributions: The evaluation of the robustness in embedded system design is a challenging task. Known approaches that consider the direct neighborhood in the solution space are not applicable due to numerous constraints and the resulting low number of feasible solutions. In contrast, an approach based on a given reference set of acceptable solutions is proposed. The focus of this paper are possible design revisions in subsequent design stages that might cause additional costs. An approach that takes arbitrary design revisions into account is presented. Since this approach is computationally expensive, an efficient algorithm based on a symbolic encoding is proposed to overcome this drawback.In order to optimize the robustness value within a design space exploration, a methodology that incorporates the robustness evaluation in the optimization process is presented. Using an external archive as ...