Smart system

Smart systems incorporate functions of sensing, actuation, and control in order to describe and analyze a situation, and make decisions based on the available data in a predictive or adaptive manner, thereby performing smart actions. In most cases the “smartness” of the system can be attributed to autonomous operation based on closed loop control, energy efficiency, and networking capabilities. Characteristics Smart systems typically consist of diverse components:

Sensors for signal acquisition
Elements transmitting the information to the command-and-control unit
Command-and-control units that take decisions and give instructions based on the available information
Components transmitting decisions and instructions
Actuators that perform or trigger the required action

Development A lot of smart systems evolved from microsystems. They combine technologies and components from microsystems technology (miniaturized electric, mechanical, optical, and fluidic dev

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Socio-technical modeling of smart energy systems: a co-simulation design for domestic energy demand

Matteo Barsanti, Claudia Rebeca Binder Signer, Lionel Guy Gérard Constantin, Pranay Kasturi

To tackle the climate crisis, the European energy strategy relies on consumers taking ownership of the energy transition, accelerating decarbonisation through investments in low-carbon technologies and ensuring system stability and reliability by actively participating in the market. Therefore, tools are needed to better understand an increasingly complex and actor-dense energy system, tracking socio-technical dynamics that occur at its margins and then predicting the effects on larger scales. Yet, existing domestic energy demand models are not flexible enough to incorporate a wide range of socio-technical factors, and to be incorporated into larger energy system simulation environments. Here, a co-simulation design for domestic energy demand modeling is presented and motivated on the basis of four design principles: granularity, scalability, modularity and transparency. Microsimulation of domestic energy demand, through the Python open source library demod, shows that it is possible to achieve high detail and high temporal resolution without compromising scalability. Furthermore, mosaik, an open source co-simulation framework, makes it possible to generate, integrate and orchestrate a multitude of demod-based instances with other independent models, which for the illustrative purposes of this study are represented by a heat pump model. The authors hope that the detailed documentation of the proposed solution will encourage interdisciplinary and collaborative efforts to develop a simulation ecosystem capable of investigating alternative energy transition pathways and evaluating policy interventions through the socio-technical lens.

Springer2021

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Gem5-X : A Many-Core Heterogeneous Simulation Platform for Architectural Exploration and Optimization

David Atienza Alonso, Yasir Mahmood Qureshi, William Andrew Simon, Marina Zapater Sancho

The increasing adoption of smart systems in our daily life has led to the development of new applications with varying performance and energy constraints, and suitable computing architectures need to be developed for these new applications. In this paper, we present gem5-X, a system-level simulation framework, based on gem-5, for architectural exploration of heterogeneous many-core systems. To demonstrate the capabilities of gem5-X, real-time video analytics is used as a case-study. It is composed of two kernels, namely, video encoding and image classification using convolutional neural networks (CNNs). First, we explore through gem5-X the benefits of latest 3D high bandwidth memory (HBM2) in different architectural configurations. Then, using a two-step exploration methodology, we develop a new optimized clustered-heterogeneous architecture with HBM2 in gem5-X for video analytics application. In this proposed clustered-heterogeneous architecture, ARMv8 in-order cluster with in-cache computing engine executes the video encoding kernel, giving 20% performance and 54% energy benefits compared to baseline ARM in-order and Out-of-Order systems, respectively. Furthermore, thanks to gem5-X we conclude that ARM Out-of-Order clusters with HBM2 are the best choice to run visual recognition using CNNs, as they outperform DDR4-based system by up to 30% both in terms of performance and energy savings.

2021

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Special Issue on Smart Sustainable Manufacturing Systems

Gökan May

2019

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