Design automation of analog circuits has long been sought. However, achieving robust and efficient analog design automation remains challenging. This article proposes a learning framework, RoSE-Opt, to achieve robust and efficient analog circuit parameter optimization. RoSE-Opt has two important features. First, it incorporates key domain knowledge of analog circuit design, such as circuit topology, couplings between circuit specifications, and variations of process, supply voltage, and temperature, into the learning loop. This strategy facilitates the training of an artificial agent capable of achieving design goals by identifying device parameters that are optimal and robust. Second, it exploits a two-level optimization method, that is, integrating Bayesian optimization (BO) with reinforcement learning (RL) to improve sample efficiency. In particular, BO is used for a coarse yet quick search of an initial starting point for optimization. This sets a solid foundation to efficiently train the RL agent with fewer samples. Experimental evaluations on benchmarking circuits show promising sample efficiency, extraordinary figure-of-merit in terms of design efficiency and design success rate, and Pareto optimality in circuit performance of our framework, compared to previous methods. Furthermore, this work thoroughly studies the performance of different RL optimization algorithms, such as deep deterministic policy gradients (DDPGs) with an off-policy learning mechanism and proximal policy optimization (PPO) with an on-policy learning mechanism. This investigation provides users with guidance on choosing the appropriate RL algorithms to optimize the device parameters of analog circuits. Finally, our study also demonstrates RoSE-Opt’s promise in parasitic-aware device optimization for analog circuits. In summary, our work reports a knowledge-infused BO-RL design automation framework for reliable and efficient optimization of analog circuits’ device parameters. Code implementation of our method can be found athttps://github.com/xz-group/RoSE.

Many real-world domains require safe decision making in uncertain environments. In this work, we introduce a deep reinforcement learning framework for approaching this important problem. We consider a distribution over transition models, and apply a risk-averse perspective towards model uncertainty through the use of coherent distortion risk measures. We provide robustness guarantees for this framework by showing it is equivalent to a specific class of distributionally robust safe reinforcement learning problems. Unlike existing approaches to robustness in deep reinforcement learning, however, our formulation does not involve minimax optimization. This leads to an efficient, model-free implementation of our approach that only requires standard data collection from a single training environment. In experiments on continuous control tasks with safety constraints, we demonstrate that our framework produces robust performance and safety at deployment time across a range of perturbed test environments.

The design automation of analog circuits is a longstanding challenge. This paper presents a reinforcement learning method enhanced by graph learning to automate the analog circuit parameter optimization at the pre-layout stage, i.e., finding device parameters to fulfill desired circuit specifications. Unlike all prior methods, our approach is inspired by human experts who rely on domain knowledge of analog circuit design (e.g., circuit topology and couplings between circuit specifications) to tackle the problem. By originally incorporating such key domain knowledge into policy training with a multimodal network, the method best learns the complex relations between circuit parameters and design targets, enabling optimal decisions in the optimization process. Experimental results on exemplary circuits show it achieves human-level design accuracy (~99%) with 1.5× efficiency of existing best-performing methods. Our method also shows better generalization ability to unseen specifications and optimality in circuit performance optimization. Moreover, it applies to design radio-frequency circuits on emerging semiconductor technologies, breaking the limitations of prior learning methods in designing conventional analog circuits.

In this paper, we investigate a Lyapunov-like differential inequality that allows us to establish finite-time stability of a continuous-time state-space dynamical system represented via a multivariate ordinary differential equation or differential inclusion. Equipped with this condition, we successfully synthesize first and second-order dynamical systems that achieve finite-time convergence to the minima of a given sufficiently regular cost function. As a byproduct, we show that the p-rescaled gradient flow (p-RGF) proposed by Wibisono et al. (2016) is indeed finite-time convergent, provided the cost function is gradient dominated of order q in (1,p). Thus, we effectively bridge a gap between the p-RGF and the normalized gradient flow (NGF) (p=\infty) proposed by Cortes (2006) in his seminal paper in the context of multi-agent systems. We discuss strategies to discretize our proposed flows and conclude by conducting some numerical experiments to illustrate our results.

The goal of this paper is to present a method for simultaneous trajectory and local stabilizing policy optimization to generate local policies for trajectory-centric model-based reinforcement learning (MBRL). This is motivated by the fact that global policy optimization for non-linear systems could be a very challenging problem both algorithmically and numerically. However, a lot of robotic manipulation tasks are trajectory-centric, and thus do not require a global model or policy. Due to inaccuracies in the learned model estimates, an open-loop trajectory optimization process mostly results in very poor performance when used on the real system. Motivated by these problems, we try to formulate the problem of trajectory optimization and local policy synthesis as a single optimization problem. It is then solved simultaneously as an instance of nonlinear programming. We provide some results for analysis as well as achieved performance of the proposed technique under some simplifying assumptions.

In this paper, we develop a dynamic mode decomposition algorithm that is robust to both inlier and outlier noise in the data. One application of our algorithm is the identification of multiple crowd or traffic flows from compressed video streams. Our method uses motion vectors that are readily available in the compressed bitstream, and do not require computationally expensive optical flow. These motion vectors are known to be very noisy, however, our algorithm is able to extract the underlying dynamical systems that define the flows. We formulate a rank regularized dynamic mode decomposition problem with total least squares constraints to estimate the Koopman modes of the motion dynamics. The estimated Koopman modes are then used to analyze the stability of the system and extract steady state and transient flows. We demonstrate the improved performance of our approach compared to state of the art schemes and illustrate it applicability in identifying transient and steady-state flows in real video sequences.

In this work we consider the problem of joint trajectory control with oscillations cancelling for planar multilink flexible manipulators. We extend a method due to Benosman et al. (2001) for the rest to rest motion problem. The scheme based on backward integration of the elastic dynamics, starting from a rest position of the flexible arms, is used here to solve the joint trajectory tracking problem. For slow joint motion, the obtained nominal elastic trajectory is used to compute a causal nominal feedforward torque permitting, together with a simple joint feedback term, to track the desired motion and damp out any residual tip-vibrations. For fast joint motion, the causal controller fails to solve the tracking problem: a noncausal feedforward torque is then designed to preset the elastic coordinates of the system, so that the causal control is able to drive the arm along the desired trajectory. The final position is then reached without end-effector residual oscillations. These results hold without considering any internal elastic damping effect. The scheme is validated through numerical and experimental results obtained on a two-links flexible planar arm.