An official website of the United States government
Analog circuit design requires substantial human expertise and involvement, which is a significant roadblock to design productivity. Bayesian Optimization (BO), a popular machine-learning-based optimization strategy, has been leveraged to automate analog design given its applicability across various circuit topologies and technologies. Traditional BO methods employ black-box Gaussian Process surrogate models and optimized labeled data queries to find optimization solutions by trading off between exploration and exploitation. However, the search for the optimal design solution in BO can be expensive from both a computational and data usage point of view, particularly for high-dimensional optimization problems. This paper presents ADO-LLM, the first work integrating large language models (LLMs) with Bayesian Optimization for analog design optimization. ADO-LLM leverages the LLM’s ability to infuse domain knowledge to rapidly generate viable design points to remedy BO's inefficiency in finding high-value design areas specifically under the limited design space coverage of the BO's probabilistic surrogate model. In the meantime, sampling of design points evaluated in the iterative BO process provides quality demonstrations for the LLM to generate high-quality design points while leveraging infused broad design knowledge. Furthermore, the diversity brought by BO's exploration enriches the contextual understanding of the LLM and allows it to more broadly search in the design space and prevent repetitive and redundant suggestions. We evaluate the proposed framework on two different types of analog circuits and demonstrate notable improvements in design efficiency and effectiveness.
more »
« less
Pareto Optimization of Analog circuits using Reinforcement Learning
Analog circuit optimization and design presents a unique set of challenges in the IC design process. Many applications require for the designer to optimize for multiple competing objectives which poses a crucial challenge. Motivated by these practical aspects, we propose a novel method to tackle multi-objective optimization for analog circuit design in continuous action spaces. In particular, we propose to: (i) Extrapolate current techniques in Multi-Objective Reinforcement Learning (MORL) to continuous state and action spaces. (ii) Provide for a dynamically tunable trained model to query user defined preferences in multi-objective optimization in the analog circuit design context.
more »
« less
- Award ID(s):
- 1956313
- PAR ID:
- 10521397
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Design Automation of Electronic Systems
- ISSN:
- 1084-4309
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
I. Farkaˇs et al. (Ed.)We propose a new deep deterministic actor-critic algorithm with an integrated network architecture and an integrated objective func- tion. We address stabilization of the learning procedure via a novel adap- tive objective that roughly ensures keeping the actor unchanged while the critic makes large errors. We reduce the number of network parame- ters and propose an improved exploration strategy over bounded action spaces. Moreover, we incorporate some recent advances in deep learn- ing to our algorithm. Experiments illustrate that our algorithm speeds up the learning process and reduces the sample complexity considerably over the state-of-the-art algorithms including TD3, SAC, PPO, and A2C in continuous control tasks.more » « less
-
Multi-objective Bayesian optimization has been widely adopted in scientific experiment design, including drug discovery and hyperparameter optimization. In practice, regulatory or safety concerns often impose additional thresholds on certain attributes of the experimental outcomes. Previous work has primarily focused on constrained single-objective optimization tasks or active search under constraints. The existing constrained multi-objective algorithms address the issue with heuristics and approximations, posing challenges to the analysis of the sample efficiency. We propose a novel constrained multi-objective Bayesian optimization algorithm COMBOO that balances active learning of the level-set defined on multiple unknowns with multi-objective optimization within the feasible region. We provide both theoretical analysis and empirical evidence, demonstrating the efficacy of our approach on various synthetic benchmarks and real-world applications.more » « less
-
Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous optimization. However, its adoption for drug design has been hindered by the discrete, high-dimensional nature of the decision variables. We develop a new approach (LaMBO) which jointly trains a denoising autoencoder with a discriminative multi-task Gaussian process head, allowing gradient-based optimization of multi-objective acquisition functions in the latent space of the autoencoder. These acquisition functions allow LaMBO to balance the explore-exploit tradeoff over multiple design rounds, and to balance objective tradeoffs by optimizing sequences at many different points on the Pareto frontier. We evaluate LaMBO on two small-molecule design tasks, and introduce new tasks optimizing \emph{in silico} and \emph{in vitro} properties of large-molecule fluorescent proteins. In our experiments LaMBO outperforms genetic optimizers and does not require a large pretraining corpus, demonstrating that BayesOpt is practical and effective for biological sequence design.more » « less
-
We investigate the energy efficiency (EE) problem in a downlink multi-user millimeter wave (mmWave) rate-splitting multiple access (RSMA) system and propose an energy-efficient one-layer RSMA hybrid precoder design for K users with quality of service constraints. This scheme is applicable to the design of sustainable sixth generation (6G) cellular networks. To make the problem tractable, the analog and the digital precoder designs are decoupled. First, the analog precoder is designed to maximize the desired signal power of each user while ignoring multi-user interference. Second, the digital precoder is designed to manage multi-user interference according to the EE optimization design criterion. We adopt a successive convex approximation-based algorithm for joint optimization of the digital precoders, power, and common rate allocation. Simulation results show that the proposed RSMA scheme always performs at least as well as a baseline spatial division multiple access (SDMA) hybrid precoding scheme and outperforms it under certain channel conditions. These results suggest that RSMA is suitable as a flexible physical layer design for future 6G mmWave networks.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3640463
