MADS: A Framework for Design and Implementation of Adaptive Digital Predistortion Systems

Li, Lin; Deaville, Peter; Sapio, Adrian; Anttila, Lauri; Valkama, Mikko; Wolf, Marilyn; Bhattacharyya, Shuvra S.

doi:10.1109/jetcas.2019.2952145

Cited by 3 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For more details on the MADS framework, including the different design components illustrated in Figure 1, we refer the reader to the presentation by Li et. al [10].…”

Section: Modeling Examplementioning

confidence: 98%

“…An example of a complex subsystem design using multiple forms of compact system-level models is the the MDP framework for Adaptive DPD (digital predistortion) Systems (MADS) [10]. DPD is a type of algorithm that is used to counteract nonlinearities in power amplifiers (PAs) to improve the quality of wireless communications signals (e.g., see [11]).…”

Section: Modeling Examplementioning

confidence: 99%

See 1 more Smart Citation

Research Challenges for Heterogeneous CPS Design

Bhattacharyya¹,

Wolf²

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Heterogeneous computing is widely used at all levels of computing from data center to edge due to its power/performance characteristics. However, heterogeneity presents challenges. Interoperability-the management of workloads across heterogeneous resources-requires more careful design than is the case for homogeneous platforms. Cyberphysical systems present additional challenges. This article considers research challenges in heterogeneous CPS design, including interoperability, physical modeling, models of computation, selfawareness and adaptation, architecture, and scheduling.

show abstract

“…For more details on the MADS framework, including the different design components illustrated in Figure 1, we refer the reader to the presentation by Li et. al [10].…”

Section: Modeling Examplementioning

confidence: 98%

Section: Modeling Examplementioning

confidence: 99%

Research Challenges for Heterogeneous CPS Design

Bhattacharyya¹,

Wolf²

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…A great deal of work has concentrated on the use of MDPs to model channel characteristics in communication systems. Li et al [140] developed a framework for the design of digital predistortion systems that optimize the communication based on the MDP models created at design time. The group also used hierarchical MDPs [141] to efficiently model both the communication environment and the computing system platform.…”

Section: B Estimate Platform and Environment Propertiesmentioning

confidence: 99%

MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

Rapp

Amrouch

Lin

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Self Cite

View full text Add to dashboard Cite

Due to the increasing size of integrated circuits (ICs), their design and optimization phases (i.e., computer-aided design, CAD) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some tradeoff between design quality and computational overhead. Machine learning (ML) can build powerful models that have successfully been employed in related domains. In this survey, we categorize how ML may be used and is used for design-time and run-time optimization and exploration strategies of ICs. A metastudy of published techniques unveils areas in computer-aided design (CAD) that are well explored and underexplored with ML, as well as trends in the employed ML algorithms. We present a comprehensive categorization and summary of the state of the art on ML for CAD. Finally, we summarize the remaining challenges and promising open research directions.

show abstract

Research Challenges for Heterogeneous Cyberphysical System Design

Bhattacharyya

Wolf

2020

Computer

View full text Add to dashboard Cite

MADS: A Framework for Design and Implementation of Adaptive Digital Predistortion Systems

Cited by 3 publications

References 15 publications

Research Challenges for Heterogeneous CPS Design

Research Challenges for Heterogeneous CPS Design

MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

Research Challenges for Heterogeneous Cyberphysical System Design

Contact Info

Product

Resources

About