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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.

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Proceedings ArticleDOI

Force-directed geographical load balancing and scheduling for batch jobs in distributed datacenters

TL;DR: A solution for load balancing and scheduling problem based on the force-directed scheduling approach is presented that considers the online application workload and limited resource and peak power capacity in each datacenter.
Proceedings Article

Energy-Aware Wireless Video Streaming.

TL;DR: A dynamic energy management policy is presented for a wireless video streaming system, consisting of battery-powered client and server, such that the maximum system lifetime is achieved while satisfying a given minimum video quality requirement.
Proceedings ArticleDOI

Design automation methodology and tools for superconductive electronics

TL;DR: This paper presents work on developing design flows and tools for DC- and AC-biased SFQ circuits, leveraging unique characteristics and design requirements of the SFQ logic families.
Proceedings ArticleDOI

Merging multiple FSM controllers for DFT/BIST hardware

TL;DR: This paper presents a technique for combining the test controllers into a minimal area merged controller and shows that this technique produces merged machines that, after state and input encoding using the minimum number of bits, have on average 33% and 24% less product terms and area, respectively.
Proceedings ArticleDOI

VIBNN: Hardware Acceleration of Bayesian Neural Networks

TL;DR: This paper proposes VIBNN, an FPGA-based hardware accelerator design for variational inference on BNNs, and introduces two high performance Gaussian (pseudo) random number generators: the RAM-based Linear Feedback Gaussian Random Number Generator (RLF-GRNG), which is inspired by the properties of binomial distribution and linear feedback logics, and the Bayesian Neural Network-oriented Wallace Gaussian random number generator.