System Level Optimization for High-Speed SerDes: Background and the Road Towards Machine Learning Assisted Design Frameworks

Song, Shiming; Sui, Yanping

doi:10.3390/electronics8111233

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…Python was selected as the developing language, since it has the feature of fast prototyping, and has a rich developer community which supports many open source libraries. These libraries include circuit simulation packages as PySpice [23] and PySerDes [24], and machine learning packages such as Tensorflow [25] and Pytorch [26]. These packages can help develop the AI-assisted full battery management system simulation framework.…”

Section: Battery Modelmentioning

confidence: 99%

A Multi-Agent Reinforcement Learning Framework for Lithium-ion Battery Scheduling Problems

Sui¹,

Song²

2020

Energies

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This paper presents a reinforcement learning framework for solving battery scheduling problems in order to extend the lifetime of batteries used in electrical vehicles (EVs), cellular phones, and embedded systems. Battery pack lifetime has often been the limiting factor in many of today’s smart systems, from mobile devices and wireless sensor networks to EVs. Smart charge-discharge scheduling of battery packs is essential to obtain super linear gain of overall system lifetime, due to the recovery effect and nonlinearity in the battery characteristics. Additionally, smart scheduling has also been shown to be beneficial for optimizing the system’s thermal profile and minimizing chances of irreversible battery damage. The recent rapidly-growing community and development infrastructure have added deep reinforcement learning (DRL) to the available tools for designing battery management systems. Through leveraging the representation powers of deep neural networks and the flexibility and versatility of reinforcement learning, DRL offers a powerful solution to both roofline analysis and real-world deployment on complicated use cases. This work presents a DRL-based battery scheduling framework to solve battery scheduling problems, with high flexibility to fit various battery models and application scenarios. Through the discussion of this framework, comparisons have also been made between conventional heuristics-based methods and DRL. The experiments demonstrate that DRL-based scheduling framework achieves battery lifetime comparable to the best weighted-k round-robin (kRR) heuristic scheduling algorithm. In the meantime, the framework offers much greater flexibility in accommodating a wide range of battery models and use cases, including thermal control and imbalanced battery.

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Section: Battery Modelmentioning

confidence: 99%

A Multi-Agent Reinforcement Learning Framework for Lithium-ion Battery Scheduling Problems

Sui¹,

Song²

2020

Energies

Self Cite

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“…System-level high-speed data link modeling and optimization have been studied intensively in recent years. Prior work such as [7], [8], and [9] primarily focused on modeling equalizers without detailed consideration of the proceeding AFE. In particular, [8] and [9] do not take the noise into account.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Photodetector/Analog Front-End Interface in Optical Communication Receivers

Radi,

Li,

Patel

et al. 2023

IEEE Trans. Signal and Power Integr.

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This paper addresses the optimization of the interface between the photodetector (PD) and the analog frontend (AFE) in high-speed, high-density optical communication receivers. Specifically, the paper focuses on optimizing design elements in the interface, including the interconnecting transmission line, the T-coil, the transimpedance amplifier (TIA), and digital equalization tap weights. To optimize the optical link, we use a combination of analytical models, electromagnetic simulations (EM), and machine learning (ML) techniques to describe different interface elements as most appropriate for each. Finally, we use the genetic algorithm to obtain optimal design parameters. The proposed optimization approach leads to a quick design time and reveals insights into some of the best design practices. As an example, we use the proposed method to investigate the relationship between optimal transmission line width and the amount of equalization available on the receiver. These conclusions are further supported by measurements taken on an assembled prototype with various PD-to-TIA interconnect lengths.

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Automation of the Error-Prone Pam-4 Sequence Discovery for the Purpose of High-Speed Serial Receiver Testing Using Reinforcement Learning Methods

Madan,

Reich,

Unakafov

et al. 2024

Communications in Computer and Information Science

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System Level Optimization for High-Speed SerDes: Background and the Road Towards Machine Learning Assisted Design Frameworks

Cited by 7 publications

References 34 publications

A Multi-Agent Reinforcement Learning Framework for Lithium-ion Battery Scheduling Problems

A Multi-Agent Reinforcement Learning Framework for Lithium-ion Battery Scheduling Problems

Optimizing the Photodetector/Analog Front-End Interface in Optical Communication Receivers

Automation of the Error-Prone Pam-4 Sequence Discovery for the Purpose of High-Speed Serial Receiver Testing Using Reinforcement Learning Methods

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