Surrogate modeling of RF circuit blocks

Surrogate models are a cost-effective replacement for expensive computer simulations in design space exploration. Literature has already demonstrated the feasibility of accurate surrogate models for single radio frequency (RF) and microwave devices. Within the European Marie Curie project O-MOORE-NICE! (Operational Model Order Reduction for Nanoscale IC Electronics) we aim to investigate the feasibility of the surrogate modeling approach for entire RF circuit blocks. This paper presents an overview about the surrogate model type selection problem for low noise amplifier modeling

An Approach for Effective Design Space Exploration of Hard-Decision Viterbi Decoder: Algorithm and VLSI Implementation

Viterbi algorithmic rule is usually used as a cryptography technique for convolutional codes, bit detection technique, Trellis in storage devices. The design space for VLSI implementation of Viterbi decoders is massive, involving selections of turnout, latency, area and power. Even for a set of parameters like constraint length, encoder polynomials and trace-back depth, the task of de-signing a Viterbi decoder is kind of troublesome and needs important effort. Sometimes, as a result of incomplete style area exploration or incorrect analysis, a suboptimal style is chosen. This work analyzes the planning complexness by applying most of the identified VLSI implementation techniques for hard-decision Viterbi cryptography to a distinct set of code parameters. The conclusions square measure supported real styles that actual synthesis and layouts were obtained. In authors’ read, as a result of the depth lined, it is the foremost comprehensive analysis of the subject revealed to this point

On Bayesian Search for the Feasible Space Under Computationally Expensive Constraints

We are often interested in identifying the feasible subset of a decision space under multiple constraints to permit effective design exploration. If determining feasibility required computationally expensive simulations, the cost of exploration would be prohibitive. Bayesian search is data-efficient for such problems: starting from a small dataset, the central concept is to use Bayesian models of constraints with an acquisition function to locate promising solutions that may improve predictions of feasibility when the dataset is augmented. At the end of this sequential active learning approach with a limited number of expensive evaluations, the models can accurately predict the feasibility of any solution obviating the need for full simulations. In this paper, we propose a novel acquisition function that combines the probability that a solution lies at the boundary between feasible and infeasible spaces (representing exploitation) and the entropy in predictions (representing exploration). Experiments confirmed the efficacy of the proposed function

Compositional design of isochronous systems

International audienceThe synchronous modeling paradigm provides strong correctness guarantees for embedded system design while requiring minimal environmental assumptions. In most related frameworks, global execution correctness is achieved by ensuring the insensitivity of (logical) time in the program from (real) time in the environment. This property, called endochrony or patience, can be statically checked, making it fast to ensure design correctness. Unfortunately, it is not preserved by composition, which makes it difficult to exploit with component-based design concepts in mind. Compositionality can be achieved by weakening this objective, but at the cost of an exhaustive state-space exploration. This raises a trade-off between performance and precision. Our aim is to balance it by proposing a formal design methodology that adheres to a weakened global design objective: the non-blocking composition of weakly endochronous processes, while preserving local design objectives for synchronous modules. This yields an effective and cost-efficient approach to compositional synchronous modeling

Multi-objective optimization of a regenerative rotorcraft powerplant: quantification of overall engine weight and fuel economy

A computationally efficient and cost effective simulation framework has been implemented to perform design space exploration and multi-objective optimization for an advanced regenerative rotorcraft powerplant configuration at mission level. The proposed framework is developed by coupling a comprehensive rotorcraft mission analysis code with a design space exploration and optimization package. The overall approach is deployed to design and optimize the powerplant of a reference twin-engine light rotorcraft, modelled after the Bo105 helicopter, manufactured by Airbus Helicopters. Firstly, a sensitivity analysis of the regenerative engine is carried out to quantify the interrelationship between the engine thermodynamic cycle design parameters, engine weight, and overall mission fuel economy. Secondly, through the execution of a multi-objective optimization strategy, a Pareto front surface is constructed, quantifying the optimum trade-off between the fuel economy offered by a regenerative engine and the associated weight penalty. The optimum sets of cycle design parameters obtained from the structured Pareto front suggest that the employed heat exchanger effectiveness is the key design parameter affecting the engine weight and fuel efficiency. Furthermore, through quantification of the benefits suggested by the acquired Pareto front, it is shown that, the fuel economy offered by the simple cycle rotorcraft engine can be substantially improved with the implementation of regeneration technology, without degrading the payload-range and airworthiness (One- Engine-Inoperative) requirements of the rotorcraft

Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration

In the current study, we introduce an integrative machine learning strategy for the autonomous molecular design of protein kinase inhibitors using variational autoencoders and a novel cluster-based perturbation approach for exploration of the chemical latent space. The proposed strategy combines autoencoder-based embedding of small molecules with a cluster-based perturbation approach for efficient navigation of the latent space and a feature-based kinase inhibition likelihood classifier that guides optimization of the molecular properties and targeted molecular design. In the proposed generative approach, molecules sharing similar structures tend to cluster in the latent space, and interpolating between two molecules in the latent space enables smooth changes in the molecular structures and properties. The results demonstrated that the proposed strategy can efficiently explore the latent space of small molecules and kinase inhibitors along interpretable directions to guide the generation of novel family-specific kinase molecules that display a significant scaffold diversity and optimal biochemical properties. Through assessment of the latent-based and chemical feature-based binary and multiclass classifiers, we developed a robust probabilistic evaluator of kinase inhibition likelihood that is specifically tailored to guide the molecular design of novel SRC kinase molecules. The generated molecules originating from LCK and ABL1 kinase inhibitors yielded ~40% of novel and valid SRC kinase compounds with high kinase inhibition likelihood probability values (p \u3e 0.75) and high similarity (Tanimoto coefficient \u3e 0.6) to the known SRC inhibitors. By combining the molecular perturbation design with the kinase inhibition likelihood analysis and similarity assessments, we showed that the proposed molecular design strategy can produce novel valid molecules and transform known inhibitors of different kinase families into potential chemical probes of the SRC kinase with excellent physicochemical profiles and high similarity to the known SRC kinase drugs. The results of our study suggest that task-specific manipulation of a biased latent space may be an important direction for more effective task-oriented and target-specific autonomous chemical design models

System design for the square kilometre array : new views of the universe

The Square Kilometre Array (SKA) radio telescope is being designed as a premier scientific instrument of the 21st century, using novel technologies to maximise its scientific capability. The SKA has an aggressive project timeline, dynamic and evolving scientific requirements, and a large design exploration space with many interdependent sub-systems. These complexities increase the difficulty in developing cost-effective design solutions that maximise the scientific capability of the telescope within construction and operations funding constraints.To gain insight into specific design challenges in this thesis, I have developed parametric models of the telescope system that relate cost to key performance metrics. I examine, as case studies, three aspects of the SKA design that have had little investigation compared to the rest of the telescope to date, but show considerable potential for discovering new astronomical phenomena.First, I present fast transient survey strategies for exploring high time resolution parameter space, and consider the system design implications of these strategies. To maximise the scientific return from limited processing capacity, I develop a new metric, ‘event rate per beam’, to measure the cost-effectiveness of the various search strategies. The most appropriate search strategy depends on the observed sky direction and the source population; for SKA Phase 1, low-frequency aperture arrays tend to be more effective for extragalactic searches, and dishes more effective for directions of increased scatter broadening, such as near the Galactic plane.Second, I compare the cost of two design solutions for low-frequency sparse aperture array observations (70–450 MHz) that achieve similar performance: a single-band implementation with a wideband antenna design; and a dual-band implementation, with each array observing approximately half the fractional bandwidth. Perhaps somewhat surprisingly, despite the dual-band array having twice the number of antenna elements, neither a representative single or dual-band implementation is cheaper a priori, although the uncertainties are currently high. In terms of the broader telescope system design, I show that the central processing, antenna deployment and site preparation costs are potentially significant cost drivers that have so far had insufficient attention.Third, the recent site decision gives rise to the question of how to cost-effectively provide data connectivity to widely separated antennas, to enable high angular resolution observations with the SKA dish array in Africa. To facilitate the design of such a data network, I parametrise the performance and cost of an exemplar network using three simple metrics: maximum baseline length; number of remote stations (grouped antennas) on long baselines; and the product of bandwidth and number of station beams. While all three metrics are cost drivers, limiting the beam–bandwidth product reduces cost without significantly impacting scientific performance.The complexities of the SKA design environment prevent straightforward analyses of cost-effective design solutions. However, the case studies in this thesis demonstrate the importance of parametric performance and cost modelling of the telescope system in determining cost-effective design solutions that are capable of revealing large regions of unexplored parameter space in the radio Universe. The analytical approach to requirements analysis and performance-cost modelling, combined with pragmatic choices to narrow the exploration space, yields new insights into cost-effective SKA designs. Continuation of this approach will be essential to successfully integrate the forthcoming results from various verifications systems into the SKA design over the next few years

Inserting New Technologies into Human-Computer Interfaces for Future Lunar and Mars Missions

Plans call for human cislunar operations and lunar surface access, to prepare for eventual Mars missions. NASA will also develop new opportunities in lunar orbit that provide the foundation and act as a gateway for human exploration deeper into the solar system. Current human spaceflight is complex and requires as many as fifty people to support the International Space Station (ISS) Mission Control Center (MCC) in Houston, Texas. These flight controllers in the front and back rooms of the MCC, serve as an extra pair of eyes overseeing the numerous station systems. Deep space missions - to the moon, Mars, and beyond - will be more complex and place challenging mission constraints on the crew. As the round-trip communication delays increase in deep space exploration, more on-board systems autonomy and functionality will be needed to maintain and control the vehicle. These mission constraints will change the Earth-based ground control approach and will demand efficient and effective human-computer interfaces (HCI) to control a highly complex vehicle or habitat system. All of this necessitates a different approach to designing and developing spacecraft and habitats. In the beginning of new human spaceflight programs, focus is typically on launch vehicle and uncrewed spacecraft design and development. The reasoning behind this focus to enable flight testing of an integrated launch vehicle and spacecraft system to ensure it will be safe enough to allow humans on board. This is an essential process for new spacecraft, however, the practical effect is a lack of funding for the spacecrafts human interfaces development. It can be many years before the human interface development begins, putting it late in the spacecraft lifecycle, when almost all other spacecraft systems and subsystems are already in place. This forces the usage of existing and proven technologies for the HCI interfaces. We posit that putting the human first in a spacecraft design process will yield a more effective spacecraft for exploration and long duration missions. NASA Human Research Program (HRP) has identified inadequate HCI as a risk for future missions. New tools and procedures to aid the crew in operating a complex spacecraft will be required. This paper discusses ongoing activities in the development of the next generation HCI components and systems, and a new approach toward human interfaces for spacecraft

Hybrid optimization approach for the design of mechanisms using a new error estimator

A hybrid optimization approach for the design of linkages is presented. The method is applied to the dimensional synthesis of mechanism and combines the merits of both stochastic and deterministic optimization. The stochastic optimization approach is based on a real-valued evolutionary algorithm (EA) and is used for extensive exploration of the design variable space when searching for the best linkage. The deterministic approach uses a local optimization technique to improve the efficiency by reducing the high CPU time that EA techniques require in this kind of applications. To that end, the deterministic approach is implemented in the evolutionary algorithm in two stages. The first stage is the fitness evaluation where the deterministic approach is used to obtain an effective new error estimator. In the second stage the deterministic approach refines the solution provided by the evolutionary part of the algorithm. The new error estimator enables the evaluation of the different individuals in each generation, avoiding the removal of well-adapted linkages that other methods would not detect. The efficiency, robustness, and accuracy of the proposed method are tested for the design of a mechanism in two examples.This paper has been developed in the framework of the Project DPI2010-18316 funded by the Spanish Ministry of Economy and Competitiveness