From Specification to Silicon: Towards Analog/Mixed-Signal Design Automation using Surrogate NN Models with Transfer Learning

Liu, Juzheng; Su, Stephen Y. H.; Madhusudan, Meghna; Hassanpourghadi, Mohsen; Saunders, S. B.; Zhang, Qiaochu; Rasul, Rezwan A; Li, Yaguang; Hu, Jiang; Sharma, Arvind Kumar; Sapatnekar, Sachin S.; Harjani, Ramesh; Levi, A. F. J.; Gupta, Sandeep; Chen, Mike Shuo-Wei

doi:10.1109/iccad51958.2021.9643445

Cited by 13 publications

(5 citation statements)

References 14 publications

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“…The main goal of the proposed algorithm is formulated in (7). As illustrated in the formulation, the algorithm searches available design spaces and finds a set of candidates that meets key constraints such as oscillation frequency, phase noise, and control voltage range.…”

Section: ) Problem Descriptionmentioning

confidence: 99%

“…As illustrated in the formulation, the algorithm searches available design spaces and finds a set of candidates that meets key constraints such as oscillation frequency, phase noise, and control voltage range. Additionally, optional constraints such as absolute maximum power consumption or the number of available phases (N ϕ in (7)) can be included to narrow down the search space. While the proposed algorithm favors the candidate that consumes the smallest power consumption (P osc ), it also outputs a set of candidates that achieves the frequency, phase noise, and control voltage constraints with additional power overheads.…”

Section: ) Problem Descriptionmentioning

confidence: 99%

“…(Search Space Construction): During the first phase of the design process, we construct a 5-dimensional search space X composed of design parameter vectors x = (l, w c , w l , n, v) to be explored. When the desired number of phases N ϕ is provided, candidates that do not meet the phase requirements in (7) are dropped out. For example, for fourphased clock generation cases (N ϕ = 4), three, five, and seven stage oscillators are removed from the search space.…”

Section: ) Problem Descriptionmentioning

confidence: 99%

“…[6] utilizes a reinforcement-learning technique with layout generators to find out the sizing parameters of analog circuits. [7] trains a neural network model to automate the circuit sizing process without running SPICE simulations after the training step. Both approaches capture the layoutdependent effects by applying transfer learning techniques.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Automated Design Methodology for Ring Voltage-Controlled Oscillators in Nanometer CMOS Technologies

et al. 2023

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This paper presents a design automation methodology for ring voltage-controlled oscillators (RVCOs) with their realistic and physical characteristics captured. With multiple sets of input constraints such as target frequency, phase noise, and control voltage range, the proposed algorithm automatically finds the design candidates that satisfy the target constraints, by running iterative post-layout simulations with auto-generated layouts and testbenches. The number of post-layout simulations is significantly reduced by the backtracking algorithm that observes the simulation results and determines the search direction. The proposed algorithm is applied to generate RVCOs in 40-nm planar and 7-nm FinFET technologies for DDR5 applications, and it turns out the proposed methodology produces sets of design parameters that meet the target specification in multiple technologies.

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Section: ) Problem Descriptionmentioning

confidence: 99%

Section: ) Problem Descriptionmentioning

confidence: 99%

Section: ) Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Automated Design Methodology for Ring Voltage-Controlled Oscillators in Nanometer CMOS Technologies

et al. 2023

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“…SPICE simulation, is drastically time-consuming. Furthermore, technology scaling has caused much longer run times in both schematic and post-layout simulations [3].…”

Section: Introductionmentioning

confidence: 99%

AnGeL: Fully-Automated Analog Circuit Generator Using a Neural Network Assisted Semi-Supervised Learning Approach

Fayazi

Taba

Afshari

et al. 2023

IEEE Trans. Circuits Syst. I

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Automatic synthesis of analog and Radio Frequency (RF) circuits is a trending approach that requires an efficient circuit modeling method. This is due to the expensive cost of running a large number of simulations at each synthesis cycle. Artificial intelligence methods are promising approaches for circuit modeling due to their speed and relative accuracy. However, existing approaches require a large amount of training data, which is still collected using simulation runs. In addition, such approaches collect a whole separate dataset for each circuit topology even if a single element is added or removed. These matters are only exacerbated by the need for post-layout modeling simulations, which take even longer. To alleviate these drawbacks, in this paper, we present FuNToM, a functional modeling method for RF circuits. FuNToM leverages the two-port analysis method for modeling multiple topologies using a single main dataset and multiple small datasets. It also leverages neural networks which have shown promising results in predicting the behavior of circuits. Our results show that for multiple RF circuits, in comparison to the state-of-the-art works, while maintaining the same accuracy, the required training data is reduced by 2.8x -10.9x. In addition, FuNToM needs 176.8x -188.6x less time for collecting the training set in post-layout modeling.

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Machine Learning for Analog Layout

Burns

Chen²,

Dhar³

et al. 2022

Machine Learning Applications in Electronic Design Automation

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From Specification to Silicon: Towards Analog/Mixed-Signal Design Automation using Surrogate NN Models with Transfer Learning

Cited by 13 publications

References 14 publications

An Automated Design Methodology for Ring Voltage-Controlled Oscillators in Nanometer CMOS Technologies

An Automated Design Methodology for Ring Voltage-Controlled Oscillators in Nanometer CMOS Technologies

AnGeL: Fully-Automated Analog Circuit Generator Using a Neural Network Assisted Semi-Supervised Learning Approach

Machine Learning for Analog Layout

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