S
Shahin Nazarian
Researcher at University of Southern California
Publications - 127
Citations - 1854
Shahin Nazarian is an academic researcher from University of Southern California. The author has contributed to research in topics: Logic gate & Smart grid. The author has an hindex of 18, co-authored 121 publications receiving 1420 citations. Previous affiliations of Shahin Nazarian include Magma Design Automation.
Papers
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Journal ArticleDOI
Thermal Modeling, Analysis, and Management in VLSI Circuits: Principles and Methods
Massoud Pedram,Shahin Nazarian +1 more
TL;DR: A brief discussion of key sources of power dissipation and their temperature relation in CMOS VLSI circuits, and techniques for full-chip temperature calculation with special attention to its implications on the design of high-performance, low-power V LSI circuits is presented.
Proceedings ArticleDOI
DRL-cloud: deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers
TL;DR: DRL-Cloud is presented, a novel Deep Reinforcement Learning (DRL)-based RP and TS system, to minimize energy cost for large-scale CSPs with very large number of servers that receive enormous numbers of user requests per day.
Journal ArticleDOI
An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study.
TL;DR: Wang et al. as mentioned in this paper proposed an in silico deep learning approach for prediction and design of a multi-epitope vaccine (DeepVacPred), which directly predicts 26 potential vaccine subunits from the available SARS-CoV-2 spike protein sequence.
Proceedings ArticleDOI
Statistical logic cell delay analysis using a current-based model
TL;DR: A new current-based cell delay model is utilized, which can accurately compute the output waveform for input waveforms of arbitrary shapes subjected to noise, and the cell parasitic capacitances are pre-characterized by lookup tables to improve the accuracy.
Journal ArticleDOI
Self-Optimizing and Self-Programming Computing Systems: A Combined Compiler, Complex Networks, and Machine Learning Approach
TL;DR: A self-optimizing and self-programming computing system (SOSPCS) design framework that achieves both programmability and flexibility and exploits computing heterogeneity and concludes that SOSPCS provides performance improvement and energy reduction compared to the state-of-the-art approaches.