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
More filters
Journal ArticleDOI
Dynamic Voltage and Frequency Scheduling for Embedded Processors Considering Power/Performance Tradeoffs
Mostafa E. Salehi,Mehrzad Samadi,Mehrdad Najibi,Ali Afzali-Kusha,Massoud Pedram,S.M. Fakhraie +5 more
TL;DR: An adaptive method to perform dynamic voltage and frequency scheduling (DVFS) for minimizing the energy consumption of microprocessor chips is presented and demonstrates considerable power savings and fewer frequency updates compared to DVFS systems based on fixed update intervals.
Proceedings ArticleDOI
Logic Extraction and Factorization for Low Power
Sasan Iman,Massoud Pedram +1 more
TL;DR: Algebraic procedures for node extraction and factorization that target low power consumption and it is shown that using the proposed SOP power cost function, all extractions resulting in a power reduction will not result in an increase in the number of literals in the network.
Proceedings ArticleDOI
TruncApp: A truncation-based approximate divider for energy efficient DSP applications
TL;DR: A high speed yet energy efficient approximate divider where the division operation is performed by multiplying the dividend by the inverse of the divisor by truncated value of the dividend is multiplied exactly by the approximate inverse value ofdivisor.
Proceedings ArticleDOI
An Analytical Model for Predicting the Remaining Battery Capacity of Lithium-Ion Batteries
Peng Rong,Massoud Pedram +1 more
TL;DR: In this paper, a closed-form analytical model for predicting the remaining capacity of a lithium-ion battery was proposed, which relies on online current and voltage measurements and correctly accounts for the temperature and cycle aging effects.
Proceedings ArticleDOI
FFT-based deep learning deployment in embedded systems
TL;DR: This work proposes a Fast Fourier Transform-based DNN training and inference model suitable for embedded platforms with reduced asymptotic complexity of both computation and storage, and develops and deploys the FFT-based inference model on embedded platforms achieving extraordinary processing speed.