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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Journal ArticleDOI
Parameterized Non-Gaussian Variational Gate Timing Analysis
TL;DR: Two new approaches for doing variational gate TA for Gaussian and non-Gaussian sources of variation in parameterized sigmaTA are presented.
Proceedings ArticleDOI
Squash: A Scalable Quantum Mapper Considering Ancilla Sharing
TL;DR: In this article, a scalable quantum mapper, called Squash, is introduced, which divides a given quantum circuit into a number of quantum kernels, each kernel comprises k parts such that each part will run on exactly one of k available cores.
Journal ArticleDOI
Design of NBTI-resilient extensible processors
TL;DR: In this paper, the effect of NBTI on the extended instruction set architecture and the arithmetic logic unit of extensible processors is studied, and the study includes modeling the circuit delay increase due to the negative bias temperature instability and its impact on the processor lifetime.
Proceedings ArticleDOI
Semi-analytical current source modeling of near-threshold operating logic cells considering process variations
TL;DR: This paper shows how to combine non-linear analytical models and low-dimensionality CSM lookup tables to simultaneously achieve modeling accuracy, space and time efficiency, when performing CSM-based timing analysis of VLSI circuits operating in near-threshold regime and subject to process variability effects.
Proceedings ArticleDOI
Variation aware dynamic power management for chip multiprocessor architectures
TL;DR: This paper adopts a Markovian Decision Process based approach to CMP power management problem, and models the underlying variability and uncertainty of parameters in system level as a partially observable MDP, and finds the optimal policy that stochastically minimizes energy per request.