M
Massoud Pedram
Researcher at University of Southern California
Publications - 812
Citations - 25236
Massoud Pedram is an academic researcher from University of Southern California. The author has contributed to research in topics: Energy consumption & CMOS. The author has an hindex of 77, co-authored 780 publications receiving 23047 citations. Previous affiliations of Massoud Pedram include University of California, Berkeley & Syracuse University.
Papers
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Proceedings ArticleDOI
Multi-objective optimization techniques for VLSI circuits
TL;DR: A variety of methods for providing analytical models for power and delay to be used in the optimization algorithms and a class of robust and scalable methods for solving multi-objective optimization problems (MOP) in a digital circuit is presented.
Proceedings ArticleDOI
Statistical sampling and regression analysis for RT-level power evaluation
TL;DR: The designer is provided with options to either improve the accuracy or the execution time when using power macro-modeling in the context of RTL simulation, and a regression estimator is described to reduce the error of the macro- modeling approach.
Journal ArticleDOI
TheSPoT: Thermal Stress-Aware Power and Temperature Management for Multiprocessor Systems-on-Chip
TL;DR: TheSPoT, a novel multilevel thermal stress-aware power and thermal management approach for MPSoCs that is more efficient in thermal stress reduction when more heterogeneous workloads are used, is presented.
Journal ArticleDOI
LETAM: A low energy truncation-based approximate multiplier
TL;DR: An energy efficient approximate multiplier design obtained by truncating the input operands is proposed, with an output quality-tunable multiplier providing ability to change the output quality during the multiplication operation.
Proceedings ArticleDOI
Energy and Performance-Aware Task Scheduling in a Mobile Cloud Computing Environment
TL;DR: This work investigates the problem of scheduling tasks (which belong to the same or possibly different applications) in an MCC environment and presents a novel algorithm, which starts from a minimal-delay scheduling solution and subsequently performs energy reduction by migrating tasks among the local cores or between theLocal cores and the cloud.