GASPAD: A General and Efficient mm-wave Integrated Circuit Synthesis Method Based on Surrogate Model Assisted Evolutionary Algorithm
Liu, Bo, Zhao, D and Gielen, G (2014) GASPAD: A General and Efficient mm-wave Integrated Circuit Synthesis Method Based on Surrogate Model Assisted Evolutionary Algorithm. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 33 (2). pp. 169-182. ISSN 0278-0070
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Liu_GASPAD_A_General_and_efficient_mm-wave_integrated_circuit_synthesis_method.pdf - Accepted Version Download (5MB) | Preview |
Abstract
The design and optimization (both sizing and layout) of mm-wave integrated circuits (ICs) have attracted much attention due to the growing demand in industry. However, available manual design and synthesis methods suffer from a high dependence on design experience, being inefficient or not general enough. To address this problem, a new method, called general mm-wave IC synthesis based on Gaussian process model assisted differential evolution (GASPAD), is proposed in this paper. A medium-scale computationally expensive constrained optimization problem must be solved for the targeted mm-wave IC design problem. Besides the basic techniques of using a global optimization algorithm to obtain highly optimized design solutions and using surrogate models to obtain a high efficiency, a surrogate model-aware search mechanism (SMAS) for tackling the several tens of design variables (medium scale) and a method to appropriately integrate constraint handling techniques into SMAS for tackling the multiple (high-) performance specifications are proposed. Experiments on two 60 GHz power amplifiers in a 65 nm CMOS technology and two mathematical benchmark problems are carried out. Comparisons with the state-of-art provide evidence of the important advantages of GASPAD in terms of solution quality and efficiency.
| Item Type: | Article |
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| Keywords: | Expensive optimization, Gaussian process, RF circuit synthesis, high-frequency integrated circuit, mm-wave integrated circuit design automation, surrogate model assisted evolutionary computation |
| Divisions: | Applied Science, Computing and Engineering |
| Depositing User: | Mr Stewart Milne |
| Date Deposited: | 11 Aug 2015 15:02 |
| Last Modified: | 26 Apr 2018 14:57 |
| URI: | https://glyndwr.repository.guildhe.ac.uk/id/eprint/8330 |
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