Direct current (DC) analysis lies at the heart of integrated circuit design in seeking DC operating points. Although pseudo-transient analysis (PTA) methods have been widely used in DC analysis in both industry and academia, their initial parameters and stepping strategy require expert knowledge and labor tuning to deliver efficient performance, which hinders their further applications. In this paper, we leverage the latest advancements in machine learning to deploy PTA with more efficient setups for different problems. More specifically, active learning, which automatically draws knowledge from other circuits, is used to provide suitable initial parameters for PTA solver, and then calibrate on-the-fly to further accelerate the simulation process using TD3-based reinforcement learning (RL). To expedite model convergence, we introduce dual agents and a public sampling buffer in our RL method to enhance sample utilization. To further improve the learning efficiency of the RL agent, we incorporate imitation learning to improve reward function and introduce supervised learning to provide a better dual-agent rotation strategy. We make the proposed algorithm a general out-of-the-box SPICE-like solver and assess it on a variety of circuits, demonstrating up to 3.10× reduction in NR iterations for the initial stage and 285.71× for the RL stage.

One of the greatest challenges in integrated circuit design is the repeated executions of computationally expensive SPICE simulations, particularly when highly complex chip testing/verification is involved. Recently, pseudo-transient analysis (PTA) has shown to be one of the most promising continuation SPICE solvers. However, the PTA efficiency is highly influenced by the inserted pseudo-parameters. In this work, we proposed BoA-PTA, a Bayesian optimization accelerated PTA that can substantially accelerate simulations and improve convergence performance without introducing extra errors. Furthermore, our method does not require any pre-computation data or offline training. The acceleration framework can either speed up ongoing, repeated simulations (e.g., Monte-Carlo simulations) immediately or improve new simulations of completely different circuits. BoA-PTA is equipped with cutting-edge machine learning techniques, such as deep learning, Gaussian process, Bayesian optimization, non-stationary monotonic transformation, and variational inference via reparameterization. We assess BoA-PTA in 43 benchmark circuits and real industrial circuits against other SOTA methods and demonstrate an average of 1.5x (maximum 3.5x) for the benchmark circuits and up to 250x speedup for the industrial circuit designs over the original CEPTA without sacrificing any accuracy.

DC analysis is the foundation for nonlinear electronic circuit simulation. Pseudo transient analysis (PTA) methods have gained great success among various continuation algorithms. However, PTA tends to be computationally intensive without careful tuning of parameters and proper stepping strategies. In this paper, we harness the latest advancing in machine learning to resolve these challenges simultaneously. Particularly, an active learning is leveraged to provide a fine initial solver environment, in which a TD3-based Reinforcement Learning (RL) is implemented to accelerate the simulation on the fly. The RL agent is strengthen with dual agents, priority sampling, and cooperative learning to enhance its robustness and convergence. The proposed algorithms are implemented in an out-of-the-box SPICElike simulator, which demonstrated a significant speedup: up to 3.1X for the initial stage and 234X for the RL stage.

Yield optimization for circuit design is computationally intensive due to the expensive yield estimation based on Monte Carlo methods and the difficult optimization process. In this work, a uniform framework to solve these problems simultaneously is proposed. Firstly, a novel efficient Bayesian yield analysis framework, BYA, is proposed by deriving a Bayesian estimation for the yield and introducing active learning based on reductions of integral entropy. A tractable convolutional entropy infill technique is then proposed to efficiently solve the entropy reduction problem. Lastly, we extend BYA for yield optimization by transforming knowledge across the design space and variational space. Experimental results based on SRAM and adder circuits show that BYA is 410x faster (in terms of the number of simulations) than standard MC and averagely 10x (up to 10000x) more accurate than the state-of-the-art method for yield estimation, and is about 5x faster than the SOTA yield optimization methods.

Most machine learning-based malware detection systems use various supervised learning methods to classify different instances of software as benign or malicious. This approach provides no information regarding the behavioral characteristics of malware. It also requires a large amount of training data and is prone to labeling difficulties and can reduce accuracy due to redundant training data. Therefore, we propose a malware detection method based on deep learning, which uses malware images and a set of autoencoders to detect malware. The method is to design an autoencoder to learn the functional characteristics of malware, and then to observe the reconstruction error of autoencoder to realize the classification and detection of malware and benign software. The proposed approach achieves 93% accuracy and comparatively better F1-score values while detecting malware and needs little training data when compared with traditional malware detection systems.