AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs

Settaluri, Keertana; Haj-Ali, Ameer; Huang, Qijing; Hakhamaneshi, Kourosh; Nikolić, Borivoje

doi:10.23919/date48585.2020.9116200

Cited by 100 publications

(49 citation statements)

References 7 publications

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“…However, the interconnect partially takes advantage of the technology scaling, because faster transistors enable a better circuit to overcome the increased channel loss. Figure 11A shows a survey from the state-of-the-art published works (Tamura et al, 2001;Haycock & Mooney, 2001;Tanaka et al, 2002;Lee et al, 2003Lee et al, , 2004Krishna et al, 2005;Landman et al, 2005;Casper et al, 2006;Palermo, Emami-Neyestanak & Horowitz, 2008;Kim et al, 2008;Lee, Chen & Wang, 2008;Amamiya et al, 2009;Chen et al, 2011;Takemoto et al, 2012;Raghavan et al, 2013;Navid et al, 2014;Zhang et al, 2015; Norimatsu et al, 2016;Gopalakrishnan et al, 2016;Shibasaki et al, 2016;Peng et al, 2017;Han et al, 2017;Upadhyaya et al, 2018;Wang et al, 2018;Depaoli et al, 2018;Tang et al, 2018;LaCroix et al, 2019;Pisati et al, 2019;Ali et al, 2019Ali et al, , 2020Im et al, 2020;Yoo et al, 2020), where we can confirm the correlation between the technology node and the data rate. On the other hand, however, overcoming the increased channel loss has become more and more expensive as the loss is going worse as the bandwidth increases; the equalization circuits consume too much power to compensate the loss, which makes people hesitant to increase the bandwidth.…”

Section: Interconnect Trend Survey and Challengessupporting

confidence: 58%

“…Recently, a dramatic change has been made to break the ice. An amplitude modulation technique, which is called 4-level pulse-amplitude modulation (PAM-4), has been adopted in the industry (Upadhyaya et al, 2018;Wang et al, 2018;Depaoli et al, 2018;Tang et al, 2018;LaCroix et al, 2019;Pisati et al, 2019;Ali et al, 2019Ali et al, , 2020Im et al, 2020;Yoo et al, 2020). With PAM-4, the interconnect can transmit two bits in one-bit period, which doubles the effective bandwidth over NRZ.…”

Section: Interconnect Trend Survey and Challengesmentioning

confidence: 99%

“…For example, it allows a machine to accomplish the entire design iteration shown in Fig. 5B ( Settaluri et al, 2020 ). Conventionally, it was believed that the design space is too huge to fully automate the optimization, even with schematic-only simulation.…”

Section: Logic (System Semiconductor)mentioning

confidence: 99%

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Today’s computing challenges: opportunities for computer hardware design

Bae

2021

PeerJ Computer Science

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Due to the explosive increase of digital data creation, demand on advancement of computing capability is ever increasing. However, the legacy approaches that we have used for continuous improvement of three elements of computer (process, memory, and interconnect) have started facing their limits, and therefore are not as effective as they used to be and are also expected to reach the end in the near future. Evidently, it is a large challenge for computer hardware industry. However, at the same time it also provides great opportunities for the hardware design industry to develop novel technologies and to take leadership away from incumbents. This paper reviews the technical challenges that today’s computing systems are facing and introduces potential directions for continuous advancement of computing capability, and discusses where computer hardware designers find good opportunities to contribute.

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Section: Interconnect Trend Survey and Challengessupporting

confidence: 58%

Section: Interconnect Trend Survey and Challengesmentioning

confidence: 99%

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Today’s computing challenges: opportunities for computer hardware design

Bae

2021

PeerJ Computer Science

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“…Compared with ANNs, DNNs are highly successful and accurate in modeling high-dimensional microwave designs [71][72][73][74][75]. For providing a bright view of using DNN in circuit designs, we clarify the DNN based optimization process presented in [42].…”

Section: Neural Network Techniquementioning

confidence: 99%

Optimization techniques for analog and RF circuit designs: an overview

Kouhalvandi

Ceylan²,

Özoğuz

2020

Analog Integr Circ Sig Process

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Optimizations have gained much consideration from the researchers working in the domains of analog and radio frequency (RF), recently. Dealing with highly nonlinear behavior of active components and aiming to meet design specifications are common issues in all nonlinear circuit designs. As a consequence and accordingly, many studies have been conducted on diverse optimization methods and algorithms for tackling the design problems and meeting optimal solutions with high accuracy. The main purpose of this article is to provide a comprehensive and systematic literature review for the optimization approaches applied by the researchers for designing various analog and microwave circuits. We focus on considering the existing optimization methods from the newly published optimization methods in the last decade. Thus, this study can guide and enlighten complementary metal-oxide-semiconductor analog and RF microwave circuit designers to consider optimization methods commonly used in both areas and to expand conventional performance figures used in their area.

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“…Neural networks (NNs) of different complexities (shallow and deep) using both supervised [10][11][12][13][14][15][16][17][18] and reinforcement [19][20][21] learning techniques have been proposed in the past six years. These methods have been boosted by the progress in the development of high-performance computing hardware and are gaining attention in the scientific community thanks to their ability to model complex nonlinear problems [22]; 2.…”

mentioning

confidence: 99%

A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation

Mina

Jabbour

Sakr³

2022

Electronics

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Analog integrated circuit design is widely considered a time-consuming task due to the acute dependence of analog performance on the transistors’ and passives’ dimensions. An important research effort has been conducted in the past decade to reduce the front-end design cycles of analog circuits by means of various automation approaches. On the other hand, the significant progress in high-performance computing hardware has made machine learning an attractive and accessible solution for everyone. The objectives of this paper were: (1) to provide a comprehensive overview of the existing state-of-the-art machine learning techniques used in analog circuit sizing and analyze their effectiveness in achieving the desired goals; (2) to point out the remaining open challenges, as well as the most relevant research directions to be explored. Finally, the different analog circuits on which machine learning techniques were applied are also presented and their results discussed from a circuit designer perspective.

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AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs

Cited by 100 publications

References 7 publications

Today’s computing challenges: opportunities for computer hardware design

Today’s computing challenges: opportunities for computer hardware design

Optimization techniques for analog and RF circuit designs: an overview

A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation

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