Shahin Nazarian

Bio: 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.

An electricity trade model for microgrid communities in smart grid

Abstract: Distributed microgrid network is the major trend of future smart grid, which contains various kinds of renewable power generation centers and a small group of energy users. In the distributed power system, each microgrid acts as a “prosumer” (producer and consumer) and maximizes its own social welfare. In addition, different microgrids can interact among each other through trading over a marketplace. In this paper, two models are introduced for microgrids to deal with the welfare maximization problems. In the first model, a microgrid is considered as a closed economy group and decides the optimal power generation distribution in terms of time. In the second model, each microgrid can trade with its neighborhoods and thus achieve a welfare increase from making use of its comparative advantage on power generation during a certain period of time. For each model, an efficient solution is presented. Experimental result shows the accuracy and efficiency of our presented solutions.

Prometheus: Processing-in-memory heterogeneous architecture design from a multi-layer network theoretic strategy

Abstract: With increasing demand for distributed intelligent physical systems performing big data analytics on the field and in real-time, processing-in-memory (PIM) architectures integrating 3D-stacked memory and logic layers could provide higher performance and energy efficiency. Towards this end, the PIM design requires principled and rigorous optimization strategies to identify interactions and manage data movement across different vaults. In this paper, we introduce Prometheus, a novel PIM-based framework that constructs a comprehensive model of computation and communication (MoCC) based on a static and dynamic compilation of an application. Firstly, by adopting a low level virtual machine (LLVM) intermediate representation (IR), an input application is modeled as a two-layered graph consisting of (i) a computation layer in which the nodes denote computation IR instructions and edges denote data dependencies among instructions, and (ii) a communication layer in which the nodes denote memory operations (e.g., load/store) and edges represent memory dependencies detected by alias analysis. Secondly, we develop an optimization framework that partitions the multi-layer network into processing communities within which the computational workload is maximized while balancing the load among computational clusters. Thirdly, we propose a community-to-vault mapping algorithm for designing a scalable hybrid memory cube (HMC)-based system where vaults are interconnected through a network-on-chip (NoC) approach rather than a crossbar architecture. This ensures scalability to hundreds of vaults in each cube. Experimental results demonstrate that Prometheus consisting of 64 HMC-based vaults improves system performance by 9.8x and achieves 2.3x energy reduction, compared to conventional systems.

There Is Hope After All: Quantifying Opinion and Trustworthiness in Neural Networks.

Abstract: Artificial Intelligence (AI) plays a fundamental role in the modern world, especially when used as an autonomous decision maker. One common concern nowadays is "how trustworthy the AIs are." Human operators follow a strict educational curriculum and performance assessment that could be exploited to quantify how much we entrust them. To quantify the trust of AI decision makers, we must go beyond task accuracy especially when facing limited, incomplete, misleading, controversial or noisy datasets. Toward addressing these challenges, we describe DeepTrust, a Subjective Logic (SL) inspired framework that constructs a probabilistic logic description of an AI algorithm and takes into account the trustworthiness of both dataset and inner algorithmic workings. DeepTrust identifies proper multi-layered neural network (NN) topologies that have high projected trust probabilities, even when trained with untrusted data. We show that uncertain opinion of data is not always malicious while evaluating NN's opinion and trustworthiness, whereas the disbelief opinion hurts trust the most. Also trust probability does not necessarily correlate with accuracy. DeepTrust also provides a projected trust probability of NN's prediction, which is useful when the NN generates an over-confident output under problematic datasets. These findings open new analytical avenues for designing and improving the NN topology by optimizing opinion and trustworthiness, along with accuracy, in a multi-objective optimization formulation, subject to space and time constraints.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Nanoscale thermal transport. II. 2003–2012

Abstract: A diverse spectrum of technology drivers such as improved thermal barriers, higher efficiency thermoelectric energy conversion, phase-change memory, heat-assisted magnetic recording, thermal management of nanoscale electronics, and nanoparticles for thermal medical therapies are motivating studies of the applied physics of thermal transport at the nanoscale. This review emphasizes developments in experiment, theory, and computation in the past ten years and summarizes the present status of the field. Interfaces become increasingly important on small length scales. Research during the past decade has extended studies of interfaces between simple metals and inorganic crystals to interfaces with molecular materials and liquids with systematic control of interface chemistry and physics. At separations on the order of ∼1 nm, the science of radiative transport through nanoscale gaps overlaps with thermal conduction by the coupling of electronic and vibrational excitations across weakly bonded or rough interface...

A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

Abstract: The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.