Universal design space exploration for building energy design

This paper describes a novel and fast placement algorithm for FPGA design space (e.g., area, power or reliability) exploration. The proposed algorithm generates the placement based on the topological similarity between two configurations (netlists) in the design space. Thus, it utilizes the sharing of reusable information during the design space exploration and avoids the time-consuming placement computation like VPR. Tested on logic-level and algorithm-level design space exploration cases, our similarity-based placement accurately depicts the “shape” of a design space and pinpoints the designs which are of most interest to IC designers. Moreover, a turbo version of circuit similarity-based placement performs an average of 30x (up to 100x) faster than VPR's while still achieving comparable placement results.

Exploration with a generative formalism must necessarily account for the nature of interaction between humans and the design space explorer. Established accounts of design interaction are made complicated by two propositions in Woodbury and Burrow's Keynote on design space exploration. First, the emphasis on the primacy of the design space as an ordered collection of partial designs (version, alternatives, extensions). Few studies exist in the design interaction literature on working with multiple threads simultaneously. Second, the need to situate, aid, and amplify human design intentions using computational tools. Although specific research and practice tools on amplification (sketching, generation, variation) have had success, there is a lack of generic, flexible, interoperable, and extensible representation to support amplification. This paper addresses the above, working with design threads and computer-assisted design amplification through a theoretical model of dialogue based on Grice's model of rational conversation. Using the concept of mixed initiative, the paper presents a visual notation for representing dialogue between designer and design space formalism through abstract examples of exploration tasks and dialogue integration.

All computing platforms, from mobile to supercomputers, are becoming more and more heterogeneous and massively parallel. While they can provide higher power efficiency and computation throughput, effective and confident use of these systems always requires knowledge about low-level programming. The average time necessary to develop and optimize a design on heterogeneous platforms is higher and higher compared to typical homogeneous systems. Dataflow models of computation (MoC) are quickly becoming the common practice in heterogeneous systems development. In domains such as signal processing and multimedia communication, dataflow MoCs have become accepted as standard. However, the shift from a sequential and architecture-specific MoC to a dataflow MoC still uncovers several programming and development challenges. The Cal Actor Language (CAL) is a recently-specified dataflow and actor-based language capable of concisely expressing complex and general purpose parallel applications. However, design tools supporting this language are generally not adequate to fully exploit its features and expressiveness power. In fact, they generally restrict its MoC in order to reduce the design space exploration (DSE) effort. The objective of this thesis is to provide a DSE methodology where all the features of CAL and dynamic dataflow MoCs can be exploited in a more general and effective manner. This dissertation illustrates a novel profiling, analysis and performance estimation methodology for the DSE of dynamic dataflow programs. The main research contributions of this thesis are: the formalization of a graph-based representation of the program execution called an execution trace graph (ETG); the formalization of a systematic methodology for profiling generic dynamic dataflow programs through their code interpretation; the formalization of a complete DSE methodology for dynamic dataflow programs in order to efficiently identify close-to-optimal design points according to various and tailored performance merit functions. In particular, the following design space optimization problems for dynamic dataflow programs are addressed: the analysis of the hotspots and the algorithmic bottlenecks of a parallel program; the bounding and optimization of the buffer size configuration for complex designs; the dynamic power dissipation minimization of programs implemented in multi-clock domain architecture. Furthermore, theoretical concepts like the design space critical path and the potential speedup of a dataflow application have been defined and revisited, respectively. The thesis also presents a DSE framework developed in order to demonstrate the effectiveness of this design methodology.

Design space exploration of microprocessor is still a challenging task for processor designers. Due to the huge design space, it is hard to determine the optimal configuration of microarchitecture parameters to satisfy the target performance and power requirements within limited time at the early stage of processor design. In this paper, a Critical Path Analysis directed Design Space Exploration (CPADSE) methodology for design space exploration of microprocessors is proposed. In CPADSE, a dependence graph model is constructed using the profile information generated during the program simulation. Then the critical paths of the dependence graph model are computed and patterns of the critical path are identified. The microarchitecture parameters mostly affecting processor`s performance or other metrics can be identified using the Plackett-Burman design method, which is always referenced as sensitivity analysis. Then the result of critical path analysis and sensitivity analysis is further used to guide the selection of search direction in both SA-CPADSE and TB-CPADSE design space search algorithms, which are proposed in this paper. Experiment result shows that for the design space exploration on SPEC 2000 benchmarks, SA-CPADSE obtains 2x speedup over baseline design space exploration algorithm SA-DSE. Additionally, TB-CPADSE on average obtains 4.3x speedup over SA-CPADSE and 7x speedup over SA-DSE. At the end of this paper, we have a detailed discussion about how to extend CPADSE methodology to design space exploration of multicore processors.

Many demanding streaming applications share functional and structural similarities with other applications in their respective domain, e.g., video analytics, software-defined radio, and radar. This opens the opportunity for specialization to achieve the needed efficiency and/or performance.Platforms integrating many accelerators (ACCs) are a primary approach for efficient, high-performance stream computing. However, designing one platform for each application is not economical due to the high costs of nonrecurring engineering (NRE) and time-to-market (TTM). To this end, the concept of domain platforms is proposed, which takes advantage of similarities across applications and designs one unified platform to accelerate a domain of applications instead of focusing on a single reference application. This dissertation approaches designing domain platforms from a function-level (kernel-level) acceleration through a heterogeneous ACC-rich platform, where each ACC is specialized to accelerate a particular function. There is a great challenge to select ACCs allocated in the domain platform, considering the large design space and performance balance across many applications. However, current Design Space Exploration (DSE) tools only focus on an individual application in isolation (e.g., one particular vision flow) for allocating a platform, but not a set of similar applications. Multiple applications are only considered in the application bindings to an existing platform. Hence, optimizations that occur due to considering multiple applications simultaneously are missed in platform design. New DSE methodologies and tools are needed with a broader scope of application sets instead of individual applications. This dissertation introduces a novel Domain Design Space Exploration (DmDSE) methodology that broadens the DSE scope from an individual application to allocating a new heterogeneous ACC-rich platform for many applications.This dissertation defines domain characteristics to express the inherent structural and functional similarities across applications and metrics to quantitatively assess domain platforms with a balanced focus across various applications. Two novel algorithms, Dynamic Score Selection (DSS) and GenetIc Domain Exploration (GIDE), are introduced to do DmDSE and allocate domain platforms, which improve performance over application-specific platforms by 58% and 75% when only considering monolithic (single function) ACCs. A finer granularity of ACCs is beneficial for generality and performance under an area constraint. Considering multi-granularity similarities across functions, Multi-Granularity DmDSE (MG-DmDSE) extends DSE through allocating finer granular and configurable ACCs and decomposing functions in the binding. MG-DmDSE produces a more general platform that benefited 87.5% of applications, while only 50% from GIDE. With a huge design space, DmDSE suffers from a long exploration time due to a large number of complex platform evaluations. Heuristics and machine learning methods are proposed to speed up DmDSE.This dissertation introduces Greedy Guided Mutation (GGM) to speed up the mutation in the GIDE algorithm, which calculates an ACC score according to current allocation to guide mutation. Alternatively, Rapid Domain Platform Performance Prediction (RDP^3) methods are introduced to replace a large number of the slow platform assessment in domain DSE, which avoids the complex application binding exploration. In the experiments, GGM reduces 84.8% of exploration time with a 0.23% loss of the final OpenVX domain platform's performance. RDP^3, using a machine learning method, yields an even more significant speedup, saving 90.8% of exploration time with only 0.0003% performance loss. DmDSE is a milestone to broaden DSE scope from individual applications to the domain level. It tremendously pushes the domain platform design from an experience-guided, manual effort into an automated process.--Author's abstract

The complexity of today's embedded systems forces designers to model and simulate systems and their components to explore the wide range of design choices. Such design space exploration is especially needed during the early design stages, where the design space is at its largest. Due to the exponential design space in real problems, evaluating and comparing every single point in the design space is infeasible. Therefore, heuristic search techniques, such as Evolutionary Algorithms (EA), are often used to search the design space for optimum design points using only a finite number of design-point evaluations. Understanding how the design space was searched by such searching algorithms and providing insight into the "landscape" of the design space, may be of invaluable importance to the designer, To this end, this paper presents a novel interactive visualization application, based on tree visualization, to understand the search dynamics of an evolutionary algorithm and to visualize where the optimum design points are located in the design space.

In a typical engineering design, there are often many design parameters to consider. Also, there are multiple competing requirements and objectives to meet. Manual approach of adjusting the parameters to achieve specific objectives is not optimal especially as the design becomes complex. In the quest for optimizing complex engineering systems, the exploration of the design space becomes imperative, especially when dealing with multi-objective systems characterized by an array of independent variables. This paper presents a comprehensive study on the design space mapping of complex engineering systems, utilizing a turboshaft engine as a case study. The initial phase of our methodology employs a physics-based model to generate synthetic dataset, reflecting the intricate interplay of various system parameters underpinning the engine's operation. This synthesized data serves as a foundation for the subsequent development of a Machine Learning or Deep Learning based surrogate model. The surrogate AI model, will be crafted to encapsulate multiple inputs and outputs inherent in the turboshaft engine's functioning, thereby facilitating an efficient and accurate exploration of the design space. Through this investigation, we will evaluate the efficacy of combining physics-based models with AI-driven techniques in mapping the design space of multi-objective systems. The core of our investigation revolves around the utilization of the AI surrogate model for achieving multi-objective optimization. This optimization process is not only focused on enhancing specific performance metrics but is also geared towards identifying a comprehensive family of feasible design solutions. Such an approach enables the delineation of the entire design space, offering invaluable insights into the trade-offs and synergies among different design objectives. Through this methodology, our goal is to uncover a wide spectrum of viable design alternatives, thereby providing a robust framework for decision-making in the engineering design process.

Model Based Systems Engineering (MBSE) is an interesting alternative to traditional systems engineering methods. Instead of using electronic documents to record system information, MBSE uses a unified and coherent system model. Trade-offs are a major element of a space systems engineer's role in early system design. This can be a particularly challenging process in the domain of spacecraft, as the system designs are often very complex and the constraints can be difficult to characterize. There has been little previous research on the use of MBSE as a design space exploration tool or in support of trade-offs. This paper investigates the potential to use MBSE for design exploration and to understand trade-offs, through the creation of a new toolset including a SysML profile. The tool draws on generative design (allowing automatic guided generation of a multitude of design alternatives) and system optimization to rapidly generate and assess new designs using interactive analysis and visualizations. Techniques such as surrogate modelling, genetic algorithms and robustness measurements will be available in the toolset. The toolset was applied to a design scenario aiming to improve the trade off and design selection process for LEO Earth observation satellites. The upcoming ESA TRUTHS space mission was used as a case study and the design process was recorded and compared to a manual design exploration approach. The toolset was found to reduce the design exploration time by 38% to 96%, allow exploration of more designs in an equivalent time and provide better quantification of the relationships present in the design space, all without drops in selected design quality. For now, the toolset can only perform parameter variation in the design exploration and future work is expected to extend this to higher levels of variability. The study also discusses how the MBSE toolset could be applied to other missions, offering the same advantages to all early phase spacecraft designers.

Methods for evaluating and covering the design space during early design development

Due to the large number of optimizations provided in modern compilers and to compiler optimization specific opportunities, a Design Space Exploration (DSE) is necessary to search for the best sequence of compiler optimizations for a given code fragment (e.g., function). As this exploration is a complex and time consuming task, in this paper we present DSE strategies to select optimization sequences to both improve the performance of each function and reduce the exploration time. The DSE is based on a clustering approach which groups functions with similarities and then explore the reduced search space provided by the optimizations previously suggested for the functions in each group. The identification of similarities between functions uses a data mining method which is applied to a symbolic code representation of the source code. The DSE process uses the reduced set identified by clustering in two ways: as the design space or as the initial configuration. In both ways, the adoption of a pre-selection based on clustering allows the use of simple and fast DSE algorithms. Our experiments for evaluating the effectiveness of the proposed approach address the exploration of compiler optimization sequences considering 49 compilation passes and targeting a Xilinx MicroBlaze processor, and were performed aiming performance improvements for 41 functions. Experimental results reveal that the use of our new clustering-based DSE approach achieved a significant reduction on the total exploration time of the search space (18x over a Genetic Algorithm approach for DSE) at the same time that important performance speedups (43% over the baseline) were obtained by the optimized codes.

A regression-based design space exploration methodology is proposed that models the impacts of the memory hierarchy and the network-on-chip (NoC) on the overall chip multiprocessor (CMP) performance. Designers cannot explore all possible designs for a NoC without considering interactions with the rest of the uncore, in particular the cache configuration and memory hierarchy which determine the amount and pattern of the traffic on the NoC. The proposed regression model is able to capture the salient design points of the uncore for a comprehensive design space exploration by designing memory and NoC-specific regression models and leveraging recent advances in uncore simulation. To show the utility of our methodology, Uncore RPD, two case studies are presented: i) analyzing and refining regression models for an 8-core CMP and ii) performing a rapid design space exploration to find best performing designs of a NoC-based CMP given area-constraints for CMPs of up to 64 cores. Through these case studies, it is shown that i) simultaneous consideration of the memory and NoC parameters in the NoC design space exploration can refine uncore-based regression models, ii) sampling techniques must consider the dynamic design space of the uncore, and iii) overall, the proposed regression models reduce the amount of simulations required to characterize the NoC design space by up to four orders of magnitude.

<div>Continuous rubber track systems for heavy applications are typically designed using multiple iterations of full-scale physical prototypes. This costly and time-consuming approach limits the possibility of exploring the design space and understanding how the design space of that kind of system is governed. A multibody dynamic simulation has recently been developed, but its complexity (due to the number of model’s inputs) makes it difficult to understand and too expensive to be used with multi-objective optimization algorithms (approximately 3 h on a desktop computer).</div> <div>This article aims to propose a first design space exploration of continuous rubber track systems via multi-objective optimization methods. Using an existing expensive multibody dynamic model as original function, surrogate models (artificial neural networks) have been trained to predict the simulation responses. These artificial neural networks are then used to explore the design space efficiently by using optimization algorithms. Sensitivity studies and multi-objective optimization were carried out on surrogate models to identify high-impact design parameters and potential improvements ranging from 2.24% to 17.2% compared to the current design of reference (CTS halftrack system) on wheel load, track pinch, and maximum required torque to cross a bump.</div>

Context: Exploring the design space is an important process in a design task. In this study, we considered design space exploration for the learners in vocational education and training (VET). The goal of the study was to investigate how they explore the design space while focusing on the effect of a graph-like interface on the learner’s understanding of the design space. With florists as the target profession, we investigated how the apprentices explore design variations, what they would gain from such activity, and how we can support this process. Approach: We developed a web application called BloomGraph that allows learners to explore design variations. It provides a graph-based interface that enables the systematic variation of design. Using the BloomGraph application, we conducted an experimental study with 44 florist apprentices in Switzerland to investigate the effect of the graph-based interface which provides a structured way of exploring the design space. The experimental group was given the graph-based interface to explore design variations while the control group had a linear-based interface. We compared them in terms of the number of bouquets explored, time of exploration, diversity of bouquets explored, and the learning gain in terms of the understanding of the design space measured using pre and post-tests. We also analyzed the strategies adopted by the participants for the graph navigation and the visual exploration behavior using the eye gaze data. Findings: Our analysis shows that the graph-based interface fosters a better understanding of the size of the design space and more efficient navigation towards a goal design in terms of the number of intermediate designs but with longer exploration of each intermediate design compared to the linear-based interface. Regarding the behavioral patterns in graph exploration, the participants who showed more strategic behavior in the design choices acquired a better understanding of the design space. Additionally, we trained a model that predicts the next choice of a learner using eye tracking data. It provides a reasonable accuracy that opens new possibilities for future studies.Conclusion: The findings of this study support the feasibility of design space exploration as a digital activity for VET learners and show how the learners can benefit from it. The contribution of the paper includes the validation of the idea with florist apprentices and the demonstration of how the process can be supported using a structured interface and the learner behavior analysis. This paper shows how a design exploration activity can provide an added value in the learning of an apprentice in a design-related VET system.

ERDSE: efficient reinforcement learning based design space exploration method for CNN accelerator on resource limited platform

The factors that influence product development depend, amongst other things, on the type of product, the economic situation, the state of the art technology and various social and cultural trends. In modern era, often not the specs and costs are the decisive factors for purchasing a product, but aspects like design and brand personality. One of the reasons for this is the abundance of products of the same type for the same purpose. In fact, this is the current situation for dry powder inhalers (DPIs) although the self-congruity concept seems more applicable to the doctors who prescribe the inhalers than to the patients who are using them. For the early DPIs from the 1960s and 1970s the driving factor for development was the necessity of having an alternative for cholofluorocarbon (CFC) containing metered dose inhalers (MDIs). An important factor affecting their design could have been the desire to improve pulmonary drug delivery compared to that from MDIs, but unfortunately this oportunity was missed. Instead, available technology like hard gelatin capsules and adhesive ('ordered') mixtures were used and lung deposition remained more or less the same as from MDIs (only approx. 10% of the dose). After the capsule (and multi-dose reservoir) DPIs became more widely accepted and patents on inhaled drugs expired, consulting groups and generic manufacturers became more and more involved in the emerging DPI market, purely on an economic basis. This does not mean that there have not been developments yet aiming at improvement of the therapy with DPIs and various future applications may give a new boost to using new technology for dry powder inhalation.