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Mobile Networks and Applications
Erschienen in:

16.05.2024 | Review

A Survey on Smart Optimisation Techniques for 6G-oriented Integrated Circuits Design

verfasst von: Thang Quoc Nguyen, Trang Hoang, Lihong Zhang, Octavia A. Dobre, Trung Q. Duong

Erschienen in: Mobile Networks and Applications | Ausgabe 6/2023

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Abstract

Der Artikel untersucht den komplizierten Prozess des Entwurfs analoger integrierter Schaltkreise (ICs) für drahtlose 6G-Netzwerke, wobei der Schwerpunkt auf der Transistorgrößenphase als signifikantem Engpass liegt. Es kategorisiert Optimierungstechniken in analytisch und simulationsbasierte Methoden, wobei letztere weiter in bayesianische, metaheuristische und verstärkungslernbasierte Ansätze unterteilt werden. Die Umfrage umfasst auch eine Fallstudie, die die Anwendung metaheuristischer Algorithmen bei der Optimierung einer Bandgap-Referenzschaltung demonstriert. Darüber hinaus werden mögliche Forschungsrichtungen wie Multiagenten-Verstärkung und Quantencomputing diskutiert, um dem Fluch der Dimensionalität bei hochdimensionalen Optimierungsproblemen zu begegnen. Der Artikel schließt mit einem Vergleich der Stärken und Grenzen verschiedener Optimierungsmethoden und schlägt zukünftige Forschungsmöglichkeiten zur Verbesserung der Automatisierung des analogen IC-Designs vor.
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Vorheriger Artikel Editorial: Towards 6G Technologies, Networks, Hardware, and Architectures
Nächster Artikel STAR-RIS-aided UAV NOMA Mobile Edge Computing Network with RF Energy Harvesting

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Literatur
1.
Mercier PP, Calhoun BH, Wang P-HP, Dissanayake A, Zhang L, Hall DA, Bowers SM (2022) Low-power rf wake-up receivers: analysis, tradeoffs, and design. IEEE Open J Solid-State Circuits Soc 2:144–164CrossRef
2.
Duong TQ, Nguyen LD, Narottama B, Ansere JA, Huynh DV, Shin H (2022) Quantum-inspired real-time optimization for 6G Networks: opportunities, challenges, and the road ahead. IEEE Open J Commun Soc 3:1347–1359CrossRef
3.
Zhang F (2020) High-speed serial buses in embedded systems. Springer, SingaporeCrossRef
4.
Huang G, Hu J, He Y, Liu J, Ma M, Shen Z, Wu J, Xu Y, Zhang H, Zhong K, Ning X, Ma Y, Yang H, Yu B, Yang H, Wang Y (2021) Machine learning for electronic design automation: a survey. ACM Trans Des Autom Electron Syst 26:5CrossRef
5.
Jiang W, Han B, Habibi MA, Schotten HD (2021) The road towards 6G: a comprehensive survey. IEEE Open J Commun Soc 2:334–366CrossRef
6.
ITU-R (2015) IMT Traffic Estimates for the Years 2020 to 2030
7.
Viswanathan H, Mogensen PE (2020) Communications in the 6G era. IEEE Access 8:57063–57074CrossRef
8.
Saad W, Bennis M, Chen M (2020) A vision of 6g wireless systems: applications, trends, technologies, and open research problems. IEEE Network 34(3):134–142CrossRef
9.
Lambrechts JW, Sinha S, Sengupta K, Bimana A, Kadam S, Bhandari S, Preez JD, Shao Z, Huang X, Liu Z, Karahan EA, Blundo T, Allam M, Ghozzy S, Zhou J, Fang W, Valliarampath J (2024) Intelligent integrated circuits and systems for 5G/6G telecommunications. IEEE Access 12:21402–21419CrossRef
10.
Rappaport TS, Xing Y, Kanhere O, Ju S, Madanayake A, Mandal S, Alkhateeb A, Trichopoulos GC (2019) Wireless communications and applications above 100 GHz: opportunities and challenges for 6g and beyond. IEEE Access 7:78729–78757CrossRef
11.
Park B, Ji Y, Sim J-Y (2020) A 490-pW SAR temperature sensor with a leakage-based Bandgap-Vth Reference. IEEE Trans Circuits Syst I Exp Briefs 67(9):1549–1553
12.
Homulle H, Sebastiano F, Charbon E (2018) Deep-Cryogenic Voltage References in 40-nm CMOS. IEEE Solid-State Circuits Lett. 1(5):110–113CrossRef
13.
Yang X-S (2018) Optimization techniques and applications with examples, 1st edn. Wiley, Hoboken, NJ, USACrossRef
14.
Razavi B (2017) Design of analog CMOS integrated circuits, 2nd edn. McGraw-Hill Education, New York
15.
Momoh J, Adapa R, El-Hawary M (1999) A review of selected optimal power flow literature to 1993. i. nonlinear and quadratic programming approaches. IEEE Trans Power Syst 14(1):96–104
16.
Momoh J, El-Hawary M, Adapa R (1999) A review of selected optimal power flow literature to 1993. ii. newton, linear programming and interior point methods. IEEE Trans Power Syst 14(1):105-111CrossRef
17.
Kim J, Lee J, Vandenberghe L, Yang C-KK (2004) Techniques for improving the accuracy of geometric-programming based analog circuit design optimization. In: Proc. IEEE/ACM Int. Conf. Comput. Aided Design, San Jose, CA, USA, pp 863–870
18.
Lui S-H, Kwan H-K, Wong N (2010) Analog circuit design by nonconvex polynomial optimization: two design examples. Int J Circuit Theor Appl 38(1):25–43CrossRef
19.
Wang Y, Orshansky M, Caramanis C (2014) Enabling efficient analog synthesis by coupling sparse regression and polynomial optimization. In: Proc. ACM/IEEE Design Autom. Conf., San Francisco, CA, USA, pp 1–6
20.
Zhou R, Poechmueller P, Wang Y (2022) An analog circuit design and optimization system with rule-guided genetic algorithm. IEEE Trans Comput-Aided Design Integr Circuits Syst 41(12):5182–5192CrossRef
21.
Tao J, Su Y, Zhou D, Zeng X, Li X (2019) Graph-constrained sparse performance modeling for analog circuit optimization via sdp relaxation. IEEE Trans Comput-Aided Design Integr Circuits Syst 38(8):1385–1398CrossRef
22.
Fortes A, da Silva Jr LA, Domanski RA, Girard A (2019) Two-stage OTA sizing optimization using bio-inspired algorithms. J Integr Circuits Syst 14(3):1–10CrossRef
23.
Vladimirescu A (1994) The SPICE book, 1st edn. John Wiley & Sons, New York, NY, USA
24.
Sanabria-Borbón A, Soto-Aguilar S, Estrada-López J, Allaire D, Sánchez-Sinencio E (2020) Gaussian-process-based surrogate for optimization-aided and process-variations-aware analog circuit design. Electronics 9(4):685CrossRef
25.
de Lima Moreto RA, Thomaz CE, Gimenez SP (2017) Gaussian fitness functions for optimizing analog cmos integrated circuits. IEEE Trans Comput-Aided Design Integr Circuits Syst 36(10):620–1632CrossRef
26.
Chen C, Wang H, Song X, Liang F, Wu K, Tao T (2022) High-dimensional bayesian optimization for analog integrated circuit sizing based on dropout and gm/ID methodology. IEEE Trans Comput-Aided Design Integr Circuits Syst 41(11):4808–4820CrossRef
27.
Zhang S, Yang F, Yan C, Zhou D, Zeng X (2022) An efficient batch-constrained bayesian optimization approach for analog circuit synthesis via multiobjective acquisition ensemble. IEEE Trans Comput-Aided Design Integr Circuits Syst 41(1):1–14CrossRef
28.
Lu J, Li Y, Yang F, Shang L, Zeng X (2023) High-level topology synthesis method for \(|Delta\)-\(\Sigma \) modulators via bi-level bayesian optimization. IEEE Trans Circuits Syst I Exp Briefs 70(12):4389–4393
29.
Lu J, Lei L, Huang J, Yang F, Shang L, Zeng X (2023) Automatic op-amp generation from specification to layout. IEEE Trans Comput-Aided Design Integr Circuits Syst 42(12):4378–4390CrossRef
30.
Choi M, Choi Y, Lee K, Kang S (2023) Reinforcement learning-based analog circuit optimizer using gm/id for sizing. In: Proc. ACM/IEEE Design Autom. Conf., San Francisco, CA, USA, pp 1–6
31.
Gu T, Li W, Zhao A, Bi Z, Li X, Yang F, Yan C, Hu W, Zhou D, Cui T, Liu X, Zhang Z, Zeng X (2024) Bbgp-sdfo: Batch bayesian and gaussian process enhanced subspace derivative free optimization for high-dimensional analog circuit synthesis. IEEE Trans Comput-Aided Design Integr Circuits Syst 43(2):417–430CrossRef
32.
Yang X-S, Deb S, Loomes M, Karamanoglu M (2013) A framework for self-tuning optimization algorithm. Neural Comput Appl 23:2051–2057CrossRef
33.
Gielen G, Walscharts H, Sansen W (1990) Analog circuit design optimization based on symbolic simulation and simulated annealing. IEEE J Solid-State Circuits 25(3):707–713CrossRef
34.
Das P, Jajodia B (2022) Design automation of two-stage operational amplifier using multi-objective genetic algorithm and spice framework. In: Proc Nepal, Int Conf Inventive Comput Tech pp 166–170
35.
Taherzadeh-Sani M, Lotfi R, Zare-Hoseini H, Shoaei O (2003) Design optimization of analog integrated circuits using simulation-based genetic algorithm. In: Proc. Int. Stmp. Signals, Circuits Syst., vol. 1, Iasi, Romania, pp 73–76
36.
Kchaou OB, Sallem A, Pereira P, Fakhfakh M, Fino MH (2015) Multi-objective sensitivity-based optimization of analog circuits exploiting nsga-ii front ranking. Proc Int Conf Synth Model Anal Simul Methods Appl Circuit Design 1–4
37.
Li Y, Wang Y, Li Y, Zhou R, Lin Z (2020) An artificial neural network assisted optimization system for analog design space exploration. IEEE Trans Comput-Aided Design Integr Circuits Syst 39(10):2640–2653CrossRef
38.
Delwar TS, Siddique A, Aras U, Ryu JY (2024) A design of adaptive genetic algorithm-based optimized power amplifier for 5G applications. Circuits Syst Signal Process 43:2–21CrossRef
39.
Rashid R, Nambath N (2022) Area optimisation of two stage Miller compensated Op-Amp in 65 nm Using Hybrid PSO. IEEE Trans Circuits Syst I Exp Briefs 69(1):199–203
40.
Raj A, Majumder S, Mishra GP (2023) Design of a CMOS based ring VCO using particle swarm optimisation. Analog Integr Circ Signal Process
41.
Rashid R, Nambath N (2021) Hybrid particle swarm optimization algorithm for area minimization in 65 nm Technology. In: Proc IEEE Int Symp Circuits Syst, Daegu, (South) Korea, pp 1–5
42.
Shreeharsha KG, Siddharth RK, Vasantha MH, Kumar YBN (2023) Partition bound random number-based particle swarm optimization for analog circuit sizing. IEEE Access 11:123577–123588CrossRef
43.
Fayazi M, Taba MT, Afshari E, Dreslinski R (2023) AnGeL: fully-automated analog circuit generator using a neural network assisted semi-supervised learning approach. IEEE Trans Circuits Syst I Reg Papers 70(11):4516–4529CrossRef
44.
Hoang T, Quoc TN, Zhang L, Duong TQ (2023) Novel methods for improved particle swarm optimization in designing the Bandgap reference circuit. IEEE Access 11:139964–139978CrossRef
45.
Fortes A, da Silva LA, Girardi A (2018) Low power bulk-driven OTA design optimization using cuckoo search algorithm. In: Proc. Symp. Integr. Circuits Syst. Design, Ben Goncalves, Brazi, pp 1–7
46.
Li C, You F, Yao T, Wang J, Shi W, Peng J, He S (2021) Simulated annealing particle swarm optimization for high-efficiency power amplifier design. IEEE Trans Microw Theory Tech 69(5):2494–2505CrossRef
47.
Joshi D, Dash S, Malhotra A, Sai PV, Das R, Sharma D, Trivedi G (2017) Optimization of 2.4 ghz cmos low noise amplifier using hybrid particle swarm optimization with lévy flight. In Proc Int Conf VLSI Design and Proc Int Conf Embedded Syst, Hyderabad, India, pp 181–186
48.
Barari M, Karimi HR, Razaghian F (2014) Analog circuit design optimization based on evolutionary algorithms. Math Problems Eng 2014
49.
Phelps R, Krasnicki M, Rutenbar R, Carley L, Hellums J (2000) Anaconda: simulation-based synthesis of analog circuits via stochastic pattern search. IEEE Trans Comput-Aided Design Integr Circuits Syst 19(6):703–717CrossRef
50.
Wang X, Wang S, Liang X, Zhao D, Huang J, Xu X, Dai B, Miao Q (2022) Deep reinforcement learning: A survey. IEEE Trans Neural Netw Learn Syst 1–15
51.
Settaluri K, Haj-Ali A, Huang Q, Hakhamaneshi K, Nikolic B (2020) Autockt: Deep reinforcement learning of analog circuit designs. In: Proc. Design, Automat. Test Europe Conf. Exhibit., Grenoble, France, pp 490–495
52.
Settaluri K, Liu Z, Khurana R, Mirhaj A, Jain R, Nikolic B (2022) Automated design of analog circuits using reinforcement learning. IEEE Trans Comput-Aided Design Integr Circuits Syst 41(9):2794–2807CrossRef
53.
Wang H, Yang J, Lee H-S, Han S (2018) Learning to design circuits. In: Proc Conf Neural Inf Process Syst Montreal, Canada
54.
Wang H, Wang K, Yang J, Shen L, Sun N, Lee H-S, Han S (2020) GCN-RL circuit designer: transferable transistor sizing with graph neural networks and reinforcement learning. In: Proc. ACM/IEEE Design Autom. Conf., San Francisco, CA, USA, pp 1–6
55.
Li Z, Carusone AC (2023) Design and optimization of low-dropout voltage regulator using relational graph neural network and reinforcement learning in open-source SKY130 Process. In: Proc San Francisco, CA, USA, Oct, IEEE/ACM Int Conf Comput Aided Design pp 01–09
56.
Hong J, Kim S, Jeon D (2022) An automatic circuit design framework for level shifter circuits. IEEE Trans Comput-Aided Design Integr Circuits Syst 41(12):5169–5181CrossRef
57.
58.
Jiang S, Zhang C, Wu W, Chen S (2019) Combined economic and emission dispatch problem of wind-thermal power system using gravitational particle swarm optimization algorithm. Math Prob Eng
59.
Yu C, Velu A, Vinitsky E, Gao J, Wang Y, Bayen A, Wu Y (2022) The surprising effectiveness of PPO in Cooperative Multi-Agent Games. In: Proc Conf Neural Inf Process Syst, New Orleans, LA, USA
60.
Duong TQ, Ansere JA, Narottama B, Sharma V, Dobre OA, Shin H (2022) Quantum-inspired machine learning for 6G: fundamentals, security, resource allocations, challenges, and future research directions. IEEE Open J Veh Technol 3:375–387
61.
Nakahara M, Ohmi T (2008) Quantum computing?: from linear algebra to physical realizations. CRC Press, USACrossRef
62.
McGeoch CC (2014) Adiabatic quantum computation and quantum annealing. Springer Cham, SwitzerlandCrossRef
63.
Ansere JA, Duong TQ, Khosravirad SR, Sharma V, Masaracchia A, Dobre OA (2023) Quantum deep reinforcement learning for 6g mobile edge computing-based IoT systems. In: Proc Marrakesh Morocco, Jul, Int Wireless Commun Mobile Comput, pp 406–411
64.
Ansere JA, Tran DT, Dobre OA, Shin H, Karagiannidis GK, Duong TQ (2024) Energy-efficient optimization for mobile edge computing with quantum machine learning. IEEE Wireless Comm Lett 13(3):661–665CrossRef
65.
Ansere JA, Gyamfi E, Sharma V, Shin H, Dobre OA, Duong TQ (2023) Quantum deep reinforcement learning for dynamic resource allocation in mobile edge computing-based IoT systems. IEEE Trans Wireless Commun 1–1
66.
Fang P-H, Chen Y-S, Wu J-S, Yu P (2024) Inverse reticle optimization with quantum annealing and hybrid solvers. IEEE Access 12:33069–33078CrossRef
67.
Zhao Z, Zhang L (2022) Deep reinforcement learning for analog circuit structure synthesis. In: Proc. Design, Automat. Test Europe Conf. Exhibit., Antwerp, Belgium, pp 1157–1160
68.
Zhao Z, Zhang L (2022) Analog integrated circuit topology synthesis with deep reinforcement learning. IEEE Trans Comput-Aided Design Integr Circuits Syst 41(12):5138–5151CrossRef
Metadaten
Titel
A Survey on Smart Optimisation Techniques for 6G-oriented Integrated Circuits Design
verfasst von
Thang Quoc Nguyen
Trang Hoang
Lihong Zhang
Octavia A. Dobre
Trung Q. Duong
Publikationsdatum
16.05.2024
Verlag
Springer US
Erschienen in
Mobile Networks and Applications / Ausgabe 6/2023
Print ISSN: 1383-469X
Elektronische ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-024-02343-7

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