Design Space Exploration Of Systems Research Articles

A novel design optimization framework to sustain remanufacturability

The ever-increasing global carbon emissions have urged the need for environmentally conscious/sustainable product design, for which the design for remanufacturing (DfRem) is one potential approach. DfRem targets at designing products that have multiple life cycles, thus significantly reducing raw material usage, energy consumption, and carbon emissions. In this paper, we develop a three-stage framework that consists of (1) systematic design space exploration and a multi-objective optimization formulation to minimize the likelihood of failure causes (such as fatigue and wear) and environmental footprint, (2) topology optimization to further reduce material usage without significantly affecting the load-carrying capability of the product, and (3) post-topology optimization design verification to ensure the proposed design satisfies all design constraints. The environmental impact can be assessed at varying comprehensiveness levels (e.g., design and manufacturing phase, use phase) and in terms of carbon or GHG emission, energy use, and waste generation. Because the novel design framework predominantly adjusted the geometry, we focused on mass-based change and energy savings due to sustained remanufacturability. The multi-objective optimization formulation in the first step results in a Pareto optimal set of possible design solutions that the designer can use for the second step. We demonstrate the utility of this framework through a case study of an engine cylinder head subjected to thermo-mechanical loads, where we find that about 5% of the product mass can be conserved with only about a 3% increase in surface area that has a fatigue life less than 10,000 cycles.

Expanding hardware accelerator system design space exploration with gem5-SALAMv2

With the prevalence of hardware accelerators as an integral part of the modern systems on chip (SoCs), the ability to model accelerators quickly and accurately within the system in which it operates is critical. This paper presents gem5-SALAMv2 as a novel system architecture for LLVM-based modeling and simulation of custom hardware accelerators integrated into the gem5 framework. It overcomes the inherent limitations of state-of-the-art trace-based pre-register-transfer level (RTL) simulators by offering a truly “execute-in-execute” LLVM-based model. It enables scalable modeling of multiple dynamically interacting accelerators with full-system simulation support. To create long-term sustainable expansion compatible with the gem5 system framework, gem5-SALAM offers a general-purpose and modular communication interface and memory hierarchy integrated into the gem5 ecosystem, streamlining designing and modeling accelerators for new and emerging applications. gem5-SALAMv2 expands upon the framework established in gem5-SALAMv1 with improved LLVM-based elaboration and simulation, improved and more extensible system integration, and new automations to simplify rapid prototyping and design space exploration. 11Conference Paper Extension: This work extends the work presented in gem5-SALAM: A System Architecture for LLVM-based Accelerator Modeling from MICRO 2020 (Rogers et al., 2020). This work expands on the aforementioned work by revamping the gem5-SALAM internals to provide more robust and extensible simulations, introducing new automation tools for expanding and simplifying design space exploration, and demonstrates the new capabilities of gem5-SALAMv2 by exploring multiple configurations of simple neural network architectures.Validation on the MachSuite (Reagen et al., 2014) benchmarks presents a timing estimation error of less than 1% against the Vivado High-Level Synthesis (HLS) tool. Results also show less than a 4% area and power estimation error against Synopsys Design Compiler. Additionally, system validation against implementations on an Ultrascale+ ZCU102 shows an average end-to-end timing error of less than 2%. Lastly, we demonstrate the upgraded capabilities of gem5-SALAMv2 by exploring accelerator platforms for two deep neural networks, LeNet5 and MobileNetv2. In these explorations, we demonstrate how gem5-SALAMv2 can simulate such systems and guide architectural optimizations for these types of accelerator-rich architectures. 22The most up-to-date version of gem5-SALAMv2 is available at https://github.com/TeCSAR-UNCC/gem5-SALAM..

Configurable sparse matrix - matrix multiplication accelerator on FPGA: A systematic design space exploration approach with quantization effects

High-performance sparse matrix multipliers are essential for deep learning applications, and as big data analytics continues to evolve, specialized accelerators are also needed to efficiently handle sparse matrix operations. This paper proposes a modified, configurable, outer product based architecture for sparse matrix multiplication, and explores design space of the proposed architecture. The performance of various architecture configurations has been examined for input samples with similar characteristics. The proposed architecture has been implemented on Xilinx Kintex-7 FPGA using 32-bit single precision floating-point arithmetic and also in 8-bit, 16-bit and 32-bit fixed-point arithmetic formats. The effect of quantization in the proposed architecture has been analyzed extensively and the results have been reported. The performance of the proposed architecture has been compared with state-of-the-art implementations, and an improvement of 9.21% has been observed in the performance.

Automated design space exploration for poultry processing systems using discrete-event simulation

Abstract The poultry processing industry struggles to keep up with new developments in meat consumption and livestock breeding. Designing poultry processing systems is becoming increasingly more complex, and an increasing number of iterations of (re)design are required to optimize the product flow in these systems. To address this issue, this study presents a discrete-event simulation-based method for design space exploration of production systems. This method is mostly automated, greatly reducing the time and effort required in the design process. The steps that are automated are iterating on the design, model construction, performing simulation experiments, and interpreting the simulation results. An industrial case study in which a poultry processing system is redesigned is used to validate the effectiveness of the proposed method. A detailed description of this case study is given to showcase the different ways in which this design space exploration method can be used.

Serverless Data Processing System and its Design Space Consideration

Serverless computing is becoming increasingly important in data-processing applications in science and business. The scheduler is at the centre of serverless data-processing systems, allowing for dynamic decisions on job and data placement. The complex design space, which is influenced by various user, cluster, and workload variables, presents problems for developing high-performance and cost-effective scheduling structures and processes. To make this exploration easier, we present Sched-Probe, a framework that includes a conceptual model and simulator for systematic design space exploration. Using the Sched-Probe framework, we evaluate the performance of three scheduling systems and two techniques using real-world workloads. Our open-source software is now available on ExDe, allowing system designers to collaborate on delving into the complexity of serverless scheduling, paving the way for optimised and efficient data-processing systems.

An in silico model of the capturing of magnetic nanoparticles in tumour spheroids in the presence of flow

One of the main challenges in improving the efficacy of conventional chemotherapeutic drugs is that they do not reach the cancer cells at sufficiently high doses while at the same time affecting healthy tissue and causing significant side effects and suffering in cancer patients. To overcome this deficiency, magnetic nanoparticles as transporter systems have emerged as a promising approach to achieve more specific tumour targeting. Drug-loaded magnetic nanoparticles can be directed to the target tissue by applying an external magnetic field. However, the magnetic forces exerted on the nanoparticles fall off rapidly with distance, making the tumour targeting challenging, even more so in the presence of flowing blood or interstitial fluid. We therefore present a computational model of the capturing of magnetic nanoparticles in a test setup: our model includes the flow around the tumour, the magnetic forces that guide the nanoparticles, and the transport within the tumour. We show how a model for the transport of magnetic nanoparticles in an external magnetic field can be integrated with a multiphase tumour model based on the theory of porous media. Our approach based on the underlying physical mechanisms can provide crucial insights into mechanisms that cannot be studied conclusively in experimental research alone. Such a computational model enables an efficient and systematic exploration of the nanoparticle design space, first in a controlled test setup and then in more complex in vivo scenarios. As an effective tool for minimising costly trial-and-error design methods, it expedites translation into clinical practice to improve therapeutic outcomes and limit adverse effects for cancer patients.Graphic

A simple, validated approach for design of two-dimensional periodic particle patterns via acoustophoresis

Two-dimensional patterning of microparticles enables a wide range of functional materials, including patterned energy storage electrodes, flexible electronics, and sensor arrays. Particle patterning via acoustics offers an attractive path to generate a wide variety of 2D periodic patterns that introduce tailorable hierarchical porosity, useful for controlling surface area, transport distances, and other properties. This method is most effective with micron scale particles and patterns of tens to hundreds of microns. To enable systematic exploration of the broad design space for such patterns, this work develops a model of 2D and 3D assembly of particles at high loadings and validates the obtained patterns against both experiments and more computationally intensive modeling techniques. Using this simple model, connections are mapped between input parameters (like actuation conditions, particle volume fraction, material properties) and output geometrical features (like void size and shape, pattern connectivity, and surface area) so that they can be tailored to given applications. The utility of this simple model is illustrated by predicting and then experimentally demonstrating new hierarchical patterns resulting from multiple waves of different frequencies interacting. These multiscale patterns offer the potential to lift the limits on surface area, diffusion distances, and other features.

A decision support system for multi-stakeholder exploration of the airship design space

Although there have been oscillations in airship interest since their use in the early 1900s, technological advancements and the need for more flexible and environmentally friendly transportation modes have caused a stream of study and surge in airship development in recent years. For companies and governments to understand how airships can be incorporated into their fleets to fulfill new or existing mission types, system design space exploration is an important step in understanding airships, their uses, and their design parameters. A decision support system (DSS), Design Exploration of Lighter-Than-Air Systems (DELTAS), was developed to help stakeholders with this task. DELTAS allows users to design airships and missions to determine how a design will perform in the scenario. As airships are sensitive to elevation and altitude, wind and terrain can also be studied to see how they may affect a mission scenario. Simulations can also be run for a given mission to find the Pareto-optimal designs for user-defined ranges of high-level airship design parameters. A case study is provided that demonstrates how DELTAS can be used to explore the airship design space for a specified mission. This case study shows how a design of experiments is important to more thoroughly cover the design space and to find and understand the relationships between airship design variables that lead to optimal mission times and costs.

Evolutionary System Design with Answer Set Programming

We address the problem of evolutionary system design (ESD) by means of answer set programming modulo difference constraints (AMT). The goal of this design approach is to synthesize new product variants or generations from existing products. We start by formalizing the underlying system synthesis problem and design space exploration process, which consists of finding the Pareto front with respect to latency, cost, energy, and similarity measures between the two designs. We then present AMT-based encodings to capture all of these aspects. The idea is to use plain ASP for conflict detection and resolution and for routing and to use difference constraints for scheduling. Moreover, we propose a new approach for expressing the similarity that we use at three alternative levels of AMT-based design space exploration, namely, at the strategic, heuristic, and objective levels, which is performed to guide the exploration towards designs of high interest. Last but not least, we systematically evaluate the emerging techniques empirically and identify the most promising AMT techniques.

Electronically Tunable Multifunction Current Mode Filter Employing Grounded Capacitors

The recent advances in Unified Modeling Language (UML) give a valuable milestone for its application to modern embedded systems design space exploration. However, it is essential to remember that UML is unable to solve the difficulty associated with embedded systems analysis, but it only provides standard modeling means. A reliable Design Space Exploration (DSE) process which suits the peculiarities of complex embedded systems design is necessary to complement the use of UML for design space exploration. In this article, we propose a Model Driven Engineering-based (MDE) co-design flow that combines high-level data-intensive application analysis with rapid prototyping. In order to specify the embedded system, our methodology relies on the Modeling and Analysis of Real-Time and Embedded Systems (MARTE) UML profile. Moreover, the present contribution uses the Parameterized and Interfaced Synchronous Dataflow (πSDF) Model-of-Computation (MoC) and a model based on the IP-XACT standard as intermediate levels of abstraction to facilitate the analysis step in the co-design flow. The rapid prototyping process relies on the πSDF graph of the application and a system-level description of the architecture. This paper presents our Hw/Sw co-specification methodology, including its support for gradual refinement of the high-level models towards lower levels of abstraction for design space exploration purposes.

A specification language for automated design space exploration of production systems

Integrating simulation in the design process of production systems allows the predicted performance of design alternatives to be compared. However, many iterations of specifying the design, constructing the simulation model, performing simulation experiments, and evaluating the simulation results for each (re)design are required. The process of specifying, modelling, simulating, and evaluating a design can be automated using a framework for automated design space exploration of production systems. This paper presents a formal specification language for the design space of a production system topology. Using the specification of the design space, feasible designs can be generated. The language supports the specification of component types, component instances, and constraints such as how many instances of a type are allowed and how components are allowed to be connected. The specification language is validated through an adaptation of an industrial case study.

Capturing Layout Dependent Effects in MOSFET Circuit Sizing Using Precomputed Lookup Tables

Sizing analog circuits using precomputed look-up tables (LUTs) has recently gained traction for fast and systematic design-space exploration without a simulator in the loop. In its current form, the underlying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g_m/I_D</i> -based design methodology assumes that the MOSFET figures of merit are independent of absolute channel width. However, this assumption can introduce significant errors when the layout-dependent effects (LDEs) of modern CMOS technologies are considered. In this paper, an accurate and efficient procedure is developed that incorporates the dependence on the MOSFET width per finger and number of fingers in the precomputed LUTs. The proposed approach uses a set of normalized auxiliary LUTs to correct the device behavior with a subtle impact on the LUT size and the computational effort. Moreover, the nonlinear variation of the drain-to-bulk ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c_db</i> ) and source-to-bulk ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c_sb</i> ) capacitances with the device number of fingers is taken into account. The correction is based on precomputed simulation data and is thus independent of the model parameters and implementation. We present a track-and-hold circuit example that is sensitive to the width independence assumption, and show that the proposed fix prevents overdesign, resulting in a 44% reduction in switch area.

Model-driven design space exploration for multi-robot systems in simulation

Multi-robot systems are increasingly deployed to provide services and accomplish missions whose complexity or cost is too high for a single robot to achieve on its own. Although multi-robot systems offer increased reliability via redundancy and enable the execution of more challenging missions, engineering these systems is very complex. This complexity affects not only the architecture modelling of the robotic team but also the modelling and analysis of the collaborative intelligence enabling the team to complete its mission. Existing approaches for the development of multi-robot applications do not provide a systematic mechanism for capturing these aspects and assessing the robustness of multi-robot systems. We address this gap by introducing ATLAS, a novel model-driven approach supporting the systematic design space exploration and robustness analysis of multi-robot systems in simulation. The ATLAS domain-specific language enables modelling the architecture of the robotic team and its mission and facilitates the specification of the team’s intelligence. We evaluate ATLAS and demonstrate its effectiveness in three simulated case studies: a healthcare Turtlebot-based mission and two unmanned underwater vehicle missions developed using the Gazebo/ROS and MOOS-IvP robotic platforms, respectively.

E2HRL: An Energy-efficient Hardware Accelerator for Hierarchical Deep Reinforcement Learning

Recently, Reinforcement Learning (RL) has shown great performance in solving sequential decision-making and control in dynamic environment problems. Despite its achievements, deploying Deep Neural Network (DNN)-based RL is expensive in terms of time and power due to the large number of episodes required to train agents with high dimensional image representations. Additionally, at the interference the large energy footprint of deep neural networks can be a major drawback. Embedded edge devices as the main platform for deploying RL applications are intrinsically resource-constrained and deploying deep neural network-based RL on them is a challenging task. As a result, reducing the number of actions taken by the RL agent to learn desired policy, along with the energy-efficient deployment of RL, is crucial. In this article, we propose Energy Efficient Hierarchical Reinforcement Learning (E2HRL), which is a scalable hardware architecture for RL applications. E2HRL utilizes a cross-layer design methodology for achieving better energy efficiency, smaller model size, higher accuracy, and system integration at the software and hardware layers. Our proposed model for RL agent is designed based on the learning hierarchical policies, which makes the network architecture more efficient for implementation on mobile devices. We evaluated our model in three different RL environments with different level of complexity. Simulation results with our analysis illustrate that hierarchical policy learning with several levels of control improves RL agents training efficiency and the agent learns the desired policy faster compared to a non-hierarchical model. This improvement is specifically more observable as the environment or the task becomes more complex with multiple objective subgoals. We tested our model with different hyperparameters to achieve the maximum reward by the RL agent while minimizing the model size, parameters, and required number of operations. E2HRL model enables efficient deployment of RL agent on resource-constraint-embedded devices with the proposed custom hardware architecture that is scalable and fully parameterized with respect to the number of input channels, filter size, and depth. The number of processing engines (PE) in the proposed hardware can vary between 1 to 8, which provides the flexibility of tradeoff of different factors such as latency, throughput, power, and energy efficiency. By performing a systematic hardware parameter analysis and design space exploration, we implemented the most energy-efficient hardware architectures of E2HRL on Xilinx Artix-7 FPGA and NVIDIA Jetson TX2. Comparing the implementation results shows Jetson TX2 boards achieve 0.1 ∼ 1.3 GOP/S/W energy efficiency while Artix-7 FPGA achieves 1.1 ∼ 11.4 GOP/S/W, which denotes 8.8× ∼ 11× better energy efficiency of E2HRL when model is implemented on FPGA. Additionally, compared to similar works our design shows better performance and energy efficiency.

Fast Design Space Exploration of Nonlinear Systems: Part II

Nonlinear system design is often a multiobjective optimization problem involving the search for a design that satisfies a number of predefined constraints. The design space is typically very large since it includes all possible system architectures with different combinations of components composing each architecture. In this article, we address nonlinear system design space exploration through a two-step approach encapsulated in a framework called fast design space exploration of nonlinear systems (ASSENT). In the first step, we use a genetic algorithm to search for system architectures that allow discrete choices for component values or else only component values for a fixed architecture. This step yields a coarse design since the system may or may not meet the target specifications. In contrast to prior works on design space exploration that rely on forward design, we use an inverse design in step 2 to search over a continuous space and fine-tune the component values with the goal of improving the value of the objective function. We use a neural network (NN) to model the system response. The NN is converted into a mixed-integer linear program for active learning to sample component values efficiently. We illustrate the efficacy of ASSENT on problems ranging from nonlinear system design to the design of electrical circuits. Experimental results show that ASSENT achieves the same or better value of the objective function compared to various other optimization techniques for nonlinear system design by up to 53%. We improve sample efficiency by 6– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$12\times $ </tex-math></inline-formula> compared to reinforcement learning-based synthesis of electrical circuits.

System Analysis and Design Space Exploration of Regional Aircraft with Electrified Powertrains

This paper explores the design spaces of a thin-haul and a regional aircraft with parallel hybrid-electric propulsion architectures for a 2030 entry into service date. Notional technology reference aircraft models were developed for a 19- and a 50-passenger aircraft based on publicly available data on Beechcraft 1900D and ATR 42-600, respectively. A set of airframe and propulsion system technologies projected to reach maturity by 2030 was infused into the aircraft models. Parametric, physics-based models were created for the charge-depleting hybrid architecture. Different modes of operation were identified and parameterized with a range of design variables to investigate the feasibility and trade space for peak power shaving, climb power boosting, electric taxi, battery usage schedules, and in-flight battery recharge strategies. Thousands of electrified aircraft concepts with varying electrification, operation, and technology scenarios were sized under the same system-level requirements as their conventional counterpart. The resulting multidisciplinary design space exploration environment was used to identify the optimum vision system designs and modes of operation for the minimum block fuel-burn objective. It has been found that both vehicle classes with the charge-depleting parallel hybrid electric architecture provided fuel-burn benefits over their 2030 advanced technology counterparts under certain operational modes.

Development and comparative selection of surrogate models using artificial neural network for an integrated regenerative transcritical cycle

Surrogate models are becoming increasingly important in replacing the computationally-expensive physics-based simulation models in many applications, such as system optimization, sensitivity analysis and design space exploration. As one of the fastest-growing field, machine learning, specifically artificial neural networks (ANN) have been adapted to model various energy systems. In the present study, five ANN-based surrogate models are developed in replacing the physics-based model of a novel regenerative transcritical power cycle using methanol as the working fluid that is integrated with a small modular reactor. The input layer of the surrogate models consists of the seven design parameters of the cycle, and the output layer returns the 1st-law efficiency, levelized cost of energy and penalty. The evaluation results show that all five candidate surrogate models have demonstrated high R2 score, low relative absolute errors (RAE) and low L1 losses, with the separate multi-layer feed-forward (MLF) neural network model outperforming the others. Once coupled with global optimization, the surrogate model is expected to find the optimal design parameters in order to minimize levelized cost of energy (LCOE) and penalty value in the system.

Codesign of Architecture, Control, and Scheduling of Modular Cyber-Physical Production Systems for Design Space Exploration

Design space exploration (DSE) of cyber-physical production systems (CPPS) is a search problem in the space of potential compositional configurations. Current design methodologies follow the separated design paradigm in which the architecture, control, and scheduling are separately designed. Optimization of each part considers only the corresponding goals of interest and overlooks other aspects by adopting gross assumptions, which makes it difficult to determine the global optimal solution for a given system. To address this problem, in this article, we propose a codesign method that considers the design spaces of architecture, control, and scheduling as monolithic, mixed discrete-continuous spaces. We formulate DSE as an optimization problem and propose a generic iterative algorithm schema involving simulation in the loop to solve the abovementioned new problem. To illustrate the effectiveness of the proposed method, a real single-stage reducer assembly production system is considered. The design results demonstrate that our method provides better solutions than does the separated design method.

Design space exploration of hysteretic negative capacitance ferroelectric FETs based on static solutions of Landau–Khalatnikov model for nonvolatile memory applications

Systematic design space exploration of negative capacitance ferroelectric field-effect transistors (FeFETs) for nonvolatile memory operations was performed, combining load line analyses and circuit simulations. Unlike those FeFETs aiming at a steep subthreshold slope, the key design target here is to achieve bi-stable current versus voltage FET characteristics with appropriate hysteresis width (i.e. memory window). Remanent polarization, coercive voltage, and interfacial layer thickness were selected as design parameters. The results show that, if a ferroelectric gate dielectric film obeying ideal single-domain Landau–Khalatnikov model dynamics with reduced remanent polarization is available, ultralow voltage nonvolatile memories operating with sub-one volt voltage swing would become possible. An interesting feature of the negative capacitance FeFETs is that, unlike conventional multiple domain FeFETs, the memory window can be adjusted to a much smaller value than twice the coercive voltage. The lowered remanent polarization is required to suppress the depolarization field to an acceptable level for reliability. It is proposed that considering the abrupt polarization switching, three-transistor and two-transistor memory cells would be suitable for working and code storage memories, respectively.

Pareto optimal design space exploration of cyber-physical systems

Cyber-physical systems (CPS) integrate a variety of engineering areas such as control, mechanical, and computer engineering in a holistic design effort. While interdependencies between the different disciplines are key attributes of CPS design science, little is known about the impact of design decisions of the cyber part on the overall performance qualities of the system. To investigate these dependencies, this paper proposes a simulation-based Design Space Exploration (DSE) framework that considers detailed cyber system parameters such as cache size, bus width, and voltage levels in addition to physical and control parameters of the CPS. We propose a DSE algorithm that explores the parameter configurations of the cyber-physical sub-system in order to approximate the Pareto-optimal design points with respect to design objectives such as energy consumption and control stability. For validation, we have successfully applied the proposed framework to an inverted-pendulum application. Here, our holistic evaluation of the Pareto-optimal points reveals the presence of non-trivial trade-offs that are imposed by the control, physical, and detailed cyber parameters. For instance, the identified energy and control optimal design points comprise configurations with a wide range of CPU speeds, sampling rates, and cache configurations following non-trivial zigzag patterns. The proposed framework could identify and manage these trade-offs and, as a result, is an imperative first step to automate the search for superior cyber-physical system configurations.