Placement is a critical step in the physical design, as it largely determines the potential for subsequent optimization. In this work, we propose a timing-driven detailed placement framework: first, a simplified RC-tree model is employed for flip-flop–buffer compensation; then, a gradient-augmented global heuristic algorithm is incorporated; and finally, timing improvement is achieved through local collaborative optimization. A comprehensive evaluation on eight ICCAD 2015 benchmarks demonstrates the effectiveness of our approach. Compared to DREAMPlace4.0-DP, a state-of-the-art timing-driven placer, our framework achieves an average improvement of 19.60% in WNS and 55.74% in TNS, while introducing less disturbance to the global placement. Moreover, it delivers a 0.80% reduction in HPWL and reduces runtime by 20.24%.

In this article, a novel data augmentation method is developed for Meta-Services based on large language models (LLMs) and requirement-functional-logical-physical (RFLP) models, referred to as generative LLM-driven data augmentation (GLIDE), and an application of GLIDE to e-commerce recommendation systems is presented. First, the framework of GLIDE is designed under Meta-Services, including a generalized encoder and decoder architecture, as well as designers, which captures the idea of “designers in the loop” in Meta-Services. Furthermore, the generalized encoder and decoder architecture is established based on RFLP models in systems engineering, aiming to translate requirements and knowledge into prompts and obtain ideal LLM outputs under physical constraints. Subsequently, the application of the GLIDE method to e-commerce recommendation systems is given in detail, which is committed to enriching users’ behavioral data and providing an effective way to decouple data augmentation and recommendation modules. Finally, recommendation experiments are performed on real-world and synthetic behavior data, and the empirical results demonstrate the effectiveness of our GLIDE method.

The tertiary structure of RNA plays a crucial role in determining its biological functions, stability, and interactions with other molecules. Accurate prediction of RNA tertiary structure is essential for understanding RNA's functional roles in cellular processes. Although the accuracy of RNA secondary structure prediction is currently considered acceptable, using these predictions as explicit geometric constraints in tertiary structure modeling remains challenges. In the study, we propose RNA3D-SSCL, an end-to-end deep learning framework for RNA tertiary structure prediction. RNA3D-SSCL leverages deep learning techniques to predict the three-dimensional folding of RNAs, incorporating a novel loss function that integrates secondary structure constraints to improve prediction performance. By utilizing a combination of sequence and secondary structure features, RNA3D-SSCL is capable of generating more accurate RNA tertiary structures. RNA3D-SSCL was evaluated on an independent test set, revealing significant improvements compared to existing methods. Ablation studies confirm that the secondary structure-constrained loss function led to a notable reduction in RMSD and an improvement in TM-score, indicating higher prediction performance. The source code can be obtained at https://github.com/CSUBioGroup/RNA3D-SSCL.

Timing optimization is critical to the entire design flow of the very-large-scale integrated (VLSI) circuit, and Global Placement is pivotal in achieving timing closure within the design flow of very-large-scale integration circuits. However, most global placement algorithms focus on optimizing wirelength rather than timing. Therefore, we propose a novel timing-driven global placement algorithm to address this gap. This paper proposes a timing-driven global placement algorithm utilizing a Nadam-based net-weighting strategy. Additionally, we employ a hybrid heuristic approach for adaptive dynamic adjustment of net weights. The experimental results on the ICCAD 2015 contest benchmarks show that compared to the RePlAce, our algorithm significantly improves WNS and TNS by 40.7% and 56.5%, respectively.

Timing-driven global placement is a critical phase in modern very-large-scale integration design, where minimizing total negative slack and worst negative slack is essential for achieving reliable timing closure. While prior works leverage pin-to-pin attraction and path-level timing analysis for optimization, their use of static weighting schemes limits adaptability to diverse critical path characteristics. In this paper, we present an enhanced timing-driven placement framework that introduces a novel entropy and node-aware pin-to-pin weighting strategy. For each pin pair, the weight is computed using its path entropy and the average number of logic nodes across associated critical paths, reflecting both slack concentration and path complexity. Furthermore, a dynamic balancing factor is incorporated to adaptively modulate the contribution of these two components during the optimization process. Experimental results on the ICCAD 2015 benchmark suite demonstrate that our approach significantly outperforms DREAMPlace 4.0, achieving an average improvement of$\mathbf{5 9. 3 9 \%}$in TNS and$\mathbf{2 7. 1 5 \%}$in WNS, along with noticeable reductions in half-perimeter wirelength.

In this paper, a novel nonzero-sum game (NSG) method is developed for completely unknown nonaffine nonlinear discrete-time (DT) systems, which is referred to as model-free NSG (MNSG). First, novel dynamic control laws are developed for NSGs using parallel control, namely introducing controls into feedback. Subsequently, an augmentedN-player NSG is formulated according to the originalN-player NSG to derive the dynamic control laws. Furthermore, we show that the control stabilities of the original and augmentedN-player NSGs are equivalent. In the meantime, we prove that optimal control of the augmentedN-player NSG is equivalent to near-optimal control of the originalN-player NSG, and the Nash equilibrium of the originalN-player NSG can be achieved. Then, a model-free learning scheme is developed to obtain the solution of the augmentedN-player NSG using online policy iteration, and neither using a model network to predict unknown dynamics nor off-policy reinforcement learning (RL) is needed in the scheme. Lastly, numerical analysis, including the NSG of a DT system with unknown control-nonaffine dynamics and coupled controls, confirms the correctness of our MNSG method. The associated code is available at: https://github.com/lujingweihh/Adaptive-dynamic-programming-algorithms/tree/main/model_free_nonzero_sum_games_discrete_time.

The subcellular localization of circular RNAs (circRNAs) is crucial for understanding their functional relevance and regulatory mechanisms. CircRNA subcellular localization exhibits variations across different cell lines, demonstrating the diversity and complexity of circRNA regulation within distinct cellular contexts. However, existing computational methods for predicting circRNA subcellular localization often ignore the importance of cell line specificity and instead train a general model on aggregated data from all cell lines. Considering the diversity and context-dependent behavior of circRNAs across different cell lines, it is imperative to develop cell line-specific models to accurately predict circRNA subcellular localization. In the study, we proposed CellCircLoc, a sequence-based deep learning model for circRNA subcellular localization prediction, which is trained for different cell lines. CellCircLoc utilizes a combination of convolutional neural networks, Transformer blocks, and bidirectional long short-term memory to capture both sequence local features and long-range dependencies within the sequences. In the Transformer blocks, CellCircLoc uses an attentive convolution mechanism to capture the importance of individual nucleotides. Extensive experiments demonstrate the effectiveness of CellCircLoc in accurately predicting circRNA subcellular localization across different cell lines, outperforming other computational models that do not consider cell line specificity. Moreover, the interpretability of CellCircLoc facilitates the discovery of important motifs associated with circRNA subcellular localization.

This article utilizes parallel control to investigate the problem of continuous-time (CT) nonzero-sum games (NZSGs) for completely unknown nonlinear systems via reinforcement learning (RL), and a parallel control-based NZSG (PNZSG) method is developed without reconstructing unknown dynamics or employing off-policy integral RL (IRL). First, novel dynamic control policies (DCPs) are developed for NZSGs by introducing controls into feedback, and an augmented system with augmented performance indices is constructed to derive the DCPs. Then, we theoretically analyze the effect of the DCPs on the control stability and performance indices, and the optimality of PNZSG is proven to be equivalent to the optimality of the original NZSGs. Subsequently, an IRL technique is employed to achieve the developed PNZSG method, and we show that no prior knowledge of the dynamics of NZSGs is needed to deploy the developed PNZSG method because of the augmented system and performance indices. Finally, numerical examples, including cooperative adaptive cruise control (CACC) of a vehicular platoon, demonstrate the correctness of the developed PNZSG method. The associated code is available at:https://github.com/lujingweihh/Adaptive-dynamic-programming-algorithms/tree/main/model_free_nonzero_sum_games.

This article uses parallel control to investigate the problem of event-triggered near-optimal control (ETNOC) for unknown discrete-time (DT) nonlinear systems. First, to achieve parallel control, an augmented nonlinear system (ANS) with an augmented performance index (API) is proposed to introduce the control input into the feedback system. The control stability relationship between the ANS and the original system is analyzed, and it is shown that, by choosing a proper API, optimal control of the ANS with the API can be seen as near-optimal control of the original system with the original performance index (OPI). Second, based on parallel control, a novel event-triggered scheme is proposed, and then a novel ETNOC method is developed using the time-triggered optimal value function of the ANS with the API. The control stability is proved, and an upper bound, which is related to the design parameter, is provided for the actual performance index in advance. Then, to implement the developed ETNOC method for unknown DT nonlinear systems, a novel online learning algorithm is developed without reconstructing unknown systems, and neural network (NN) and adaptive dynamic programming (ADP) techniques are employed in the developed algorithm. The convergence of the signals in the closed-loop system (CLS) is shown using the Lyapunov approach, and the assumption of boundedness of input dynamics is not required. Finally, two simulations justify the theoretical conjectures.

Aiming to provide a novel paradigm of oil fields, metaverses-based parallel oil fields are proposed in this article. Compared with the existing smart/intelligent oil fields in cyber–physical systems (CPS), parallel oil fields can take human factors into full consideration and expand the operation space to cyber–physical–social systems (CPSS), which can be regarded as the abstract and scientific explanation of metaverses. In the proposed parallel oil fields, there are three kinds of workers (human workers, digital workers, and robotic workers) coordinating to construct a more reliable and intelligent oil field. Furthermore, the framework and methodology of parallel oil fields are illustrated by parallel systems and the artificial systems, computational experiments, and parallel executions (ACP) approach. Based on the proposed framework, parallel oil fields are capable of generating a more trustworthy artificial system and guaranteeing the realization of the 6S (safety, security, sustainability, sensitivity, service, and smartness) goal. Finally, based on dynamometer cards, fault diagnosis of sucker rod pumping systems (SRPS) is investigated in parallel oil fields.

In this article, we study the optimal control problem of continuous-time (CT) time-invariant nonlinear systems with stochastic nonlinear disturbances. A new stochastic adaptive dynamic programming (ADP) method is developed to solve the Hamilton–Jacobi–Bellman equation (HJBE). Under the conditional expectation, the value function and the control law are successively approximated simultaneously. The asymptotic stability of the closed-loop stochastic system in probability is analyzed by the stochastic Lyapunov direct method, and the convergence of the developed ADP method is given. Finally, four simulations illustrate the effectiveness of the developed method.

In cyber–physical–social systems, smart manufacturing has to overcome challenges, such as uncertainty, diversity, complexity in modeling, long-delayed responses to market changes, and human engineer dependency. DeFACT is a framework of parallel manufacturing in ManuVerse where the Decentralized Autonomous Organization-based interactions between parallel workers consisting of robotic, digital, and human workers are elaborated to transform from professional division to real-virtual division. In DeFACT, human workers are only responsible for 5% physical and mental work that is complex and creative, and the robotic and digital workers can take care of the rest. The perceptual and cognitive intelligence of digital workers are intensified by a manufacturing foundation model (MF-PC), where calibration and certification (C&C), and verification and validation (V&V) guarantee not only the accuracy of task models, but also the interpretability and controllability of feature learning. As a case study, the workflow of customized shoes of SANBODY Technology Company is illustrated to show how DeFACT breaks the time and space constraints, avoids production waste caused by aesthetic discrepancies with consumers, and truly realizes flexible manufacturing.

This article presents a novel event-triggered near-optimal control (ETNOC) method for discrete-time (DT) constrained nonlinear systems. First, the tracking error system is constructed to convert the tracking control problem to the regulation problem. By introducing the tracking error system, the asymmetric control constraints design for the original constrained system can be converted to the symmetric control constraints design for the tracking error system. Second, a novel triggering condition is developed using the time-triggered optimal value function and control law. It is proven that the closed-loop system (CLS) is asymptotically stable under the developed ETNOC method, and there exists a predetermined upper bound for the real performance index. Then, to implement the developed ETNOC method, a parallel control approach with neural networks (NNs) and adaptive dynamic programming techniques is proposed to predict the next state of the system and obtain the optimal value function and control law. The stability analysis of the CLS is provided in the consideration of the estimation errors of the NN weights and state. Finally, the effectiveness of the developed ETNOC method is validated by an application to a boiler-turbine system.

Many complex nonlinear optimization or control issues can be transformed into the solving of time-varying nonlinear equations (TVNEs), playing a fundamental role in the control and management of complex systems. As a result, a robust and high-precision online solution method is significant for TVNE. However, there are three main challenges for handling TVNE via the existing methods: First, short-time invariance assumption frequently leveraged in the existing methods leads to lagging errors that are difficult to eliminate. Second, it is difficult in dealing with unknown noise disturbance during the solution process, which causes low solution accuracy or solution failure. Third, existing continuous-time methods are hard to be implemented on digital equipments. In this article, an anti-noise discrete-time neural dynamics (DTND) is designed and studied to overcome the above issues systematically. The theoretical analysis and numerical simulations demonstrate that the proposed model effectively eliminates the lagging errors and achieves the accurate solution of the TVNE in a noisy environment. Moreover, to verify the superior numerical computational property of the DTND model, the intention recognition of lower limbs is explored from the artificial systems, computational experiments, and parallel execution (ACP) framework. Specifically, a nonlinear artificial dynamic system (NADS) concerning the human surface electromyogram (sEMG) signals and joint information is established, which performs in parallel with the actual human lower limb physical experiments. Simulation results illustrate that, within the acceptable range of the digital computer, the controller designed by the DTND model can well guide the NADS to accurately recognize the motion intention of the human lower limb.

A novel event-triggered optimal tracking control (ETOTC) method is developed for discrete-time nonlinear systems in this study. For the time-invariant desired trajectory, we prove that the tracking error is asymptotically stable, and an upper bound of the real performance index can be predetermined by a design parameter. For the time-varying desired trajectory, the developed triggering condition reduces communication costs by relaxing the restriction of the asymptotic stability of the closed-loop system, and we prove that the tracking error is uniformly ultimately bounded (UUB). The developed ETOTC method entails obtaining the next state of the real system. Therefore, a parallel control approach is proposed to predict the next state by constructing a parallel system for the real system. Neural networks (NNs) and adaptive dynamic programming (ADP) techniques are utilized in the parallel control approach. Moreover, the stability analysis of the closed-loop system is shown, and the tracking error and NN weight estimation errors are proved to be UUB using the Lyapunov approach. Finally, we validate the developed ETOTC method through two simulations.

In this article, a new self-learning parallel control method, which is based on adaptive dynamic programming (ADP) technique, is developed for solving the optimal control problem of discrete- time time-varying nonlinear systems. It aims to obtain an approximate optimal control law sequence and simultaneously guarantees the convergence of the value function. Establishing the time-varying artificial system by neural networks in a certain time-horizon, a control-sequence-improvement ADP algorithm is developed to obtain the control law sequence. For the first time, the criteria of the parallel execution are presented, such that the value function is proven to converge to a finite neighborhood of the optimal performance index function. Finally, numerical results and analysis are presented to demonstrate the effectiveness of the parallel control method.

We propose a flat, analytic, mixed-size placement algorithm ePlace-3D for three-dimension integrated circuits (3D-ICs) using nonlinear optimization. Our contributions are (1) electrostatics based 3D density function with globally uniform smoothness (2) 3D numerical solution with improved spectral formulation (3) 3D nonlinear pre-conditioner for convergence acceleration (4) interleaved 2D-3D placement for efficiency enhancement. Our placer outperforms the leading work mPL6-3D and NTUplace3-3D with 6.44% and 37.15% shorter wirelength, 9.11% and 10.27% fewer 3D vertical interconnects (VI) on average of IBM-PLACE circuits. Validation on the large-scale modern mixed-size (MMS) 3D circuits shows high performance and scalability.

We propose an electrostatics-based placement algorithm for large-scale mixed-size circuits (ePlace-MS). ePlace-MS is generalized, flat, analytic and nonlinear. The density modeling method eDensity is extended to handle the mixed-size placement. We conduct detailed analysis on the correctness of the gradient formulation and the numerical solution, as well as the rationale of dc removal and the advantages over prior density functions. Nesterov's method is used as the nonlinear solver, which shows high yet stable performance over mixed-size circuits. The steplength is set as the inverse of Lipschitz constant of the gradient function, while we develop a backtracking method to prevent overestimation. An approximated nonlinear preconditioner is developed to minimize the topological and physical differences between large macros and standard cells. Besides, we devise a simulated annealer to legalize the layout of macros and use a second-phase global placement to reoptimize the standard cell layout. All the above innovations are integrated into our mixed-size placement prototype ePlace-MS, which outperforms all the related works in literature with better quality and efficiency. Compared to the leading-edge mixed-size placer NTUplace3, ePlace-MS produces up to 22.98% and on average 8.22% shorter wirelength over all the 16 modern mixed-size benchmark circuits with the same runtime.

We develop a flat, analytic, and nonlinear placement algorithm, ePlace , which is more effective, generalized, simpler, and faster than previous works. Based on the analogy between placement instance and electrostatic system, we develop a novel placement density function eDensity , which models every object as positive charge and the density cost as the potential energy of the electrostatic system. The electric potential and field distribution are coupled with density using a well-defined Poisson's equation, which is numerically solved by spectral methods based on fast Fourier transform (FFT). Instead of using the conjugate gradient (CG) nonlinear solver in previous placers, we propose to use Nesterov's method which achieves faster convergence. The efficiency bottleneck on line search is resolved by predicting the steplength using a closed-form equation of Lipschitz constant. The placement performance is validated through experiments on the ISPD 2005 and ISPD 2006 benchmark suites, where ePlace outperforms all state-of-the-art placers (Capo10.5, FastPlace3.0, RQL, MAPLE, ComPLx, BonnPlace, POLAR, APlace3, NTUPlace3, mPL6) with much shorter wirelength and shorter or comparable runtime. On average, of all the ISPD 2005 benchmarks, ePlace outperforms the leading placer BonnPlace with 2.83% shorter wirelength and runs 3.05× faster; and on average, of all the ISPD 2006 benchmarks, ePlace outperforms the leading placer MAPLE with 4.59% shorter wirelength and runs 2.84× faster.

ePlace is a generalized analytic algorithm to handle large-scale standard-cell and mixed-size placement. We use a novel density function based on electrostatics to remove overlap and Nesterov's method to minimize the nonlinear cost. Steplength is estimated as the inverse of Lipschitz constant, which is determined by our dynamic prediction and backtracking method. An approximated preconditioner is proposed to resolve the difference between large macros and standard cells, while an annealing engine is devised to handle macro legalization followed by placement of standard cells. The above innovations are integrated into our placement prototype ePlace, which outperforms the leading-edge placers on respective standard-cell and mixed-size benchmark suites. Specifically, ePlace produces 2.83%, 4.59% and 7.13% shorter wirelength while runs 3.05×, 2.84× and 1.05× faster than BonnPlace, MAPLE and NTUplace3-unified in average of ISPD 2005, ISPD 2006 and MMS circuits, respectively.

Clock tree synthesis plays an important role on the total performance of chip. Gated clock tree is an effective approach to reduce the dynamic power usage. In this paper, two novel gated clock tree synthesizers, power-aware clock tree synthesizer (PACTS) and power- and slew-aware clock tree synthesizer (PSACTS), are proposed with zero skew achieved based on Elmore RC model. In PACTS, the topology of the clock tree is constructed with simultaneous buffer/gate insertion, which reduces the switched capacitance. In PSACTS, a more practical clock slew constraint is applied. Compared to previous works, clock tree synthesis is done first and followed by the insertions of clock gates. The clock slew changes a lot after the insertions of clock gates in real cases. In our work, the clock tree is constructed simultaneously with the insertions of clock gates. This ensures the limitation of the clock slew can be strictly satisfied while the limitation of the clock slew is always applied in the real design. The experimental results show that the power cost of our work is smaller and the runtime is reduced. The slew rate constraint is satisfied with a small clock skew from SPICE estimation. Generally, our work has better performance, improved efficiency and is more practical to be applied in the industry.

In nanometer-scale VLSI physical design, clock network becomes a major concern on determining the total performance of digital circuit. Clock skew and PVT (process, voltage and temperature) variations contribute a lot to its behavior. Previous works mainly focused on skew and wirelength minimization. It may lead to negative influence towards these process variation factors. In this paper, a novel clock network synthesizer is proposed and several algorithms are introduced for performance improvement. A dual-MST (DMST) geometric matching approach is proposed for topology construction. It can help balancing the tree structure to reduce the variation effect. A recursive buffer insertion technique and a blockage handling method are also presented, and they are developed for proper distribution of buffers and saving of capacitance. Experimental results show that our matching approach is better than the traditional methods, and in particular our synthesizer has better performance compared to the results of the winner in the ISPD 2009 contest.

Routability optimization has become a major concern in physical design of VLSI circuits. Due to the recent advances in VLSI technology, interconnect has become a dominant factor of the overall performance of a circuit. In order to optimize interconnect cost, we need a good congestion estimation method to predict routability in the early designing stages. Many congestion models have been proposed but there's still a lot of room for improvement. Besides, routers will perform rip-up and reroute operations to prevent overflow, but most models do not consider this case. The outcome is that the existing models will usually underestimate the routability. In this paper, we have a comprehensive study on our proposed congestion models. Results show that the estimation results of our approaches are always more accurate than the previous congestion models.