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result(s) for
"design space exploration"
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HW/SW co-design of dedicated heterogeneous parallel systems: an extended design space exploration approach
This work faces the problem of the hardware/software (HW/SW) co-design of dedicated electronic digital systems based on heterogeneous parallel architectures. In particular, other than describing the reference HW/SW co-design flow, it proposes an extension of a previous system-level design space exploration approach able to suggest to the designer an HW/SW partitioning of the system functionalities specification and a mapping of the partitioned entities onto an automatically defined ‘heterogeneous multi multi-core processor’ architecture.
Journal Article
The design and engineering of Curiosity : how the Mars rover performs its job
This book describes the most complex machine ever sent to another planet: Curiosity. It is a one-ton robot with two brains, seventeen cameras, six wheels, nuclear power, and a laser beam on its head. No one human understands how all of its systems and instruments work. This essential reference to the Curiosity mission explains the engineering behind every system on the rover, from its rocket-powered jetpack to its radioisotope thermoelectric generator to its fiendishly complex sample handling system. Its lavishly illustrated text explains how all the instruments work -- its cameras, spectrometers, sample-cooking oven, and weather station -- and describes the instruments' abilities and limitations. It tells you how the systems have functioned on Mars, and how scientists and engineers have worked around problems developed on a faraway planet: holey wheels and broken focus lasers. And it explains the grueling mission operations schedule that keeps the rover working day in and day out.
Book
A Case for Security-Aware Design-Space Exploration of Embedded Systems
As modern embedded systems are becoming more and more ubiquitous and interconnected, they attract a world-wide attention of attackers and the security aspect is more important than ever during the design of those systems. Moreover, given the ever-increasing complexity of the applications that run on these systems, it becomes increasingly difficult to meet all security criteria. While extra-functional design objectives such as performance and power/energy consumption are typically taken into account already during the very early stages of embedded systems design, system security is still mostly considered as an afterthought. That is, security is usually not regarded in the process of (early) design-space exploration of embedded systems, which is the critical process of multi-objective optimization that aims at optimizing the extra-functional behavior of a design. This position paper argues for the development of techniques for quantifying the ’degree of secureness’ of embedded system design instances such that these can be incorporated in a multi-objective optimization process. Such technology would allow for the optimization of security aspects of embedded systems during the earliest design phases as well as for studying the trade-offs between security and the other design objectives such as performance, power consumption and cost.
Journal Article
Quantifying the maximum possible improvement in$$2^{k}$$experiments
This research formulates, and numerically quantifies the optimal response that can be discovered in a design space characterized by main effects, and two-way and three-way interactions. In an experimental design setup, this can be conceptualized as the response of the best treatment combination of a$$2^k$$2 k full factorial design. Using Gaussian and Uniform priors for the strength of main effects and interaction effects, this study enables a practitioner to make estimates of the maximum possible improvement that is possible through design space exploration. For basic designs up to two factors, we construct the full distribution of the optimal treatment. Whereas, for values of$$k\\ge 3$$k ≥ 3 , we analytically formulate two indicators of a greedy heuristic of the expected value of the optimal treatment. We present results for these formulations up to$$k=7$$k = 7 factors and validate these through simulations. Finally, we also present an illustrative case study of the power loss in disengaged wet clutches, which confirms our findings and serves as an implementation guide for practitioners.
Journal Article
Flare: An FPGA-Based Full Precision Low Power CNN Accelerator with Reconfigurable Structure
Convolutional neural networks (CNNs) have significantly advanced various fields; however, their computational demands and power consumption have escalated, posing challenges for deployment in low-power scenarios. To address this issue and facilitate the application of CNNs in power constrained environments, the development of dedicated CNN accelerators is crucial. Prior research has predominantly concentrated on developing low precision CNN accelerators using code generated from high-level synthesis (HLS) tools. Unfortunately, these approaches often fail to efficiently utilize the computational resources of field-programmable gate arrays (FPGAs) and do not extend well to full precision scenarios. To overcome these limitations, we integrate vector dot products to unify the convolution and fully connected layers. By treating the row vector of input feature maps as the fundamental processing unit, we balance processing latency and resource consumption while eliminating data rearrangement time. Furthermore, an accurate design space exploration (DSE) model is established to identify the optimal design points for each CNN layer, and dynamic partial reconfiguration is employed to maximize each layer’s access to computational resources. Our approach is validated through the implementation of AlexNet and VGG16 on 7A100T and ZU15EG platforms, respectively. We achieve an average convolutional layer throughput of 28.985 GOP/s and 246.711 GOP/s for full precision. Notably, the proposed accelerator demonstrates remarkable power efficiency, with a maximum improvement of 23.989 and 15.376 times compared to current state-of-the-art FPGA implementations.
Journal Article
Design space exploration and optimization using self-organizing maps
Identifying regions of interest (RoI) in the design space is extremely useful while building metamodels with limited computational budget. Self-organizing maps (SOM) are used as a visualization technique for design space exploration that permits identifying RoI. Conventional implementation of SOM is susceptible to folds or intersections that hinder visualizing the design space. This work proposes a modified SOM algorithm whose maps are interpretable and that does not fold and allows smoother input and performance space visualization. The modified algorithm enables identification of RoI and additional sampling in the identified RoI allows building accurate Kriging metamodel, which is then used for optimization. The proposed approach is demonstrated on benchmark nonlinear analytical examples and two practical engineering design examples. Results show that the proposed approach is highly efficient in identifying the RoI and in obtaining the optima with less samples.
Journal Article
The Business Process Design Space for exploring process redesign alternatives
PurposeProcess redesign refers to the intentional change of business processes. While process redesign methods provide structure to redesign projects, they provide limited support during the actual creation of to-be processes. More specifically, existing approaches hardly develop an ontological perspective on what can be changed from a process design point of view, and they provide limited procedural guidance on how to derive possible process design alternatives. This paper aims to provide structured guidance during the to-be process creation.Design/methodology/approachUsing design space exploration as a theoretical lens, the authors develop a conceptual model of the design space for business processes, which facilitates the systematic exploration of design alternatives along different dimensions. The authors utilized an established method for taxonomy development for constructing the conceptual model. First, the authors derived design dimensions for business processes and underlying characteristics through a literature review. Second, the authors conducted semi-structured interviews with professional process experts. Third, the authors evaluated their artifact through three real-world applications.FindingsThe authors identified 19 business process design dimensions that are grouped into different layers and specified by underlying characteristics. Guiding questions and illustrative real-world examples help to deploy these design dimensions in practice. Taken together, the design dimensions form the “Business Process Design Space” (BPD-Space).Research limitations/implicationsPractitioners can use the BPD-Space to explore, question and rethink business processes in various respects.Originality/valueThe BPD-Space complements existing approaches by explicating process design dimensions. It abstracts from specific process flows and representations of processes and supports an unconstrained exploration of various alternative process designs.
Journal Article
A novel visualization enabled decision support framework for data-driven integrated design space exploration
Design preferences or targets are typically available at the system level. A designer is usually interested in understanding patches of the design space at component levels, across different stages and processes that correspond to such system targets or preferences. This demands a thorough design space exploration permitting both forward and inverse designs. Such exploration becomes cumbersome with a large number of variables and complex systems with many conflicting goals. Hence a decision support framework that permits seamless navigation in high dimensions, especially with a visual aspect for enhanced comprehension, is desirable. Current work proposes using a data-driven interpretable self-organizing map (iSOM) as a visual enabler in decision support systems for exploring the design space and understanding the trade-off in system goals. The novelty lies in being able to use a visual form to compare greater than three conflicting goals simultaneously while accounting for design variables. The proposed approach is demonstrated using two test problems: (i) hot rolling and cooling process chain design for the production of steel rods, and (ii) head and neck injury risk evaluations for vehicular crash-worthiness. Using the first problem, we demonstrate the capability of iSOM to support the solution space exploration of a many-goal steel manufacturing process chain problem to realize the design of a steel product, in the context of a compromise Decision Support Problem formulation. In the second problem, we demonstrate the capability of iSOM to support early stage Design Space Exploration (DSE) to identify critical injury risk regions of interest for different car crash scenarios. These two test problems illustrate the capability to carry out a forward and inverse design, by the proposed approach.
Journal Article
Multi-objective design space exploration using explainable surrogate models
The surrogate model is an essential part of modern design optimization and exploration. In some cases, exploration of design space in multi-objective problems is important to reveal useful design insight and guidelines that will be useful for engineers. However, most surrogate models are black boxes, making interpretation difficult. This paper investigates the framework of explainable surrogate models using Shapley Additive Explanations (SHAP) to gain important design insight that helps users better understand the relationship between objective functions and design variables. We applied the explainable surrogate model framework to multi-objective design problems and performed a comparison with active subspaces and Sobol indices. Several techniques to extract design insight based on SHAP values are discussed: the averaged SHAP, the SHAP summary plot, the single- and bi-objective SHAP dependence plot, and the SHAP correlation matrix. Two aerodynamic design cases are selected to demonstrate the capability of explainable surrogate models: nine-variable inviscid and twenty-variable viscous transonic airfoil design. The findings indicate that SHAP provides more valuable insights than active subspaces and Sobol indices, particularly regarding the impact of individual design variables on the objectives. Consequently, SHAP can be employed in conjunction with active subspaces and Sobol indices to explore the input–output relationship in multi-objective design exploration comprehensively.
Journal Article