35 research outputs found

    Fault-based Analysis of Industrial Cyber-Physical Systems

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    The fourth industrial revolution called Industry 4.0 tries to bridge the gap between traditional Electronic Design Automation (EDA) technologies and the necessity of innovating in many indus- trial fields, e.g., automotive, avionic, and manufacturing. This complex digitalization process in- volves every industrial facility and comprises the transformation of methodologies, techniques, and tools to improve the efficiency of every industrial process. The enhancement of functional safety in Industry 4.0 applications needs to exploit the studies related to model-based and data-driven anal- yses of the deployed Industrial Cyber-Physical System (ICPS). Modeling an ICPS is possible at different abstraction levels, relying on the physical details included in the model and necessary to describe specific system behaviors. However, it is extremely complicated because an ICPS is com- posed of heterogeneous components related to different physical domains, e.g., digital, electrical, and mechanical. In addition, it is also necessary to consider not only nominal behaviors but even faulty behaviors to perform more specific analyses, e.g., predictive maintenance of specific assets. Nevertheless, these faulty data are usually not present or not available directly from the industrial machinery. To overcome these limitations, constructing a virtual model of an ICPS extended with different classes of faults enables the characterization of faulty behaviors of the system influenced by different faults. In literature, these topics are addressed with non-uniformly approaches and with the absence of standardized and automatic methodologies for describing and simulating faults in the different domains composing an ICPS. This thesis attempts to overcome these state-of-the-art gaps by proposing novel methodologies, techniques, and tools to: model and simulate analog and multi-domain systems; abstract low-level models to higher-level behavioral models; and monitor industrial systems based on the Industrial Internet of Things (IIOT) paradigm. Specifically, the proposed contributions involve the exten- sion of state-of-the-art fault injection practices to improve the ICPSs safety, the development of frameworks for safety operations automatization, and the definition of a monitoring framework for ICPSs. Overall, fault injection in analog and digital models is the state of the practice to en- sure functional safety, as mentioned in the ISO 26262 standard specific for the automotive field. Starting from state-of-the-art defects defined for analog descriptions, new defects are proposed to enhance the IEEE P2427 draft standard for analog defect modeling and coverage. Moreover, dif- ferent techniques to abstract a transistor-level model to a behavioral model are proposed to speed up the simulation of faulty circuits. Therefore, unlike the electrical domain, there is no extensive use of fault injection techniques in the mechanical one. Thus, extending the fault injection to the mechanical and thermal fields allows for supporting the definition and evaluation of more reliable safety mechanisms. Hence, a taxonomy of mechanical faults is derived from the electrical domain by exploiting the physical analogies. Furthermore, specific tools are built for automatically instru- menting different descriptions with multi-domain faults. The entire work is proposed as a basis for supporting the creation of increasingly resilient and secure ICPS that need to preserve functional safety in any operating context

    2022 roadmap on neuromorphic computing and engineering

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    Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018^{18} calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

    Mining Technologies Innovative Development

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    The present book covers the main challenges, important for future prospects of subsoils extraction as a public effective and profitable business, as well as technologically advanced industry. In the near future, the mining industry must overcome the problems of structural changes in raw materials demand and raise the productivity up to the level of high-tech industries to maintain the profits. This means the formation of a comprehensive and integral response to such challenges as the need for innovative modernization of mining equipment and an increase in its reliability, the widespread introduction of Industry 4.0 technologies in the activities of mining enterprises, the transition to "green mining" and the improvement of labor safety and avoidance of man-made accidents. The answer to these challenges is impossible without involving a wide range of scientific community in the publication of research results and exchange of views and ideas. To solve the problem, this book combines the works of researchers from the world's leading centers of mining science on the development of mining machines and mechanical systems, surface and underground geotechnology, mineral processing, digital systems in mining, mine ventilation and labor protection, and geo-ecology. A special place among them is given to post-mining technologies research

    SpiNNaker - A Spiking Neural Network Architecture

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    20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come

    Intelligent Circuits and Systems

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    ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.  This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

    Advanced Sensors for Real-Time Monitoring Applications

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    It is impossible to imagine the modern world without sensors, or without real-time information about almost everything—from local temperature to material composition and health parameters. We sense, measure, and process data and act accordingly all the time. In fact, real-time monitoring and information is key to a successful business, an assistant in life-saving decisions that healthcare professionals make, and a tool in research that could revolutionize the future. To ensure that sensors address the rapidly developing needs of various areas of our lives and activities, scientists, researchers, manufacturers, and end-users have established an efficient dialogue so that the newest technological achievements in all aspects of real-time sensing can be implemented for the benefit of the wider community. This book documents some of the results of such a dialogue and reports on advances in sensors and sensor systems for existing and emerging real-time monitoring applications

    Recent Development of Hybrid Renewable Energy Systems

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    Abstract: The use of renewable energies continues to increase. However, the energy obtained from renewable resources is variable over time. The amount of energy produced from the renewable energy sources (RES) over time depends on the meteorological conditions of the region chosen, the season, the relief, etc. So, variable power and nonguaranteed energy produced by renewable sources implies intermittence of the grid. The key lies in supply sources integrated to a hybrid system (HS)

    STT-MRAM characterization and its test implications

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    Spin torque transfer (STT)-magnetoresistive random-access memory (MRAM) has come a long way in research to meet the speed and power consumption requirements for future memory applications. The state-of-the-art STT-MRAM bit-cells employ magnetic tunnel junction (MTJ) with perpendicular magnetic anisotropy (PMA). The process repeatabil- ity and yield stability for wafer fabrication are some of the critical issues encountered in STT-MRAM mass production. Some of the yield improvement techniques to combat the e ect of process variations have been previously explored. However, little research has been done on defect oriented testing of STT-MRAM arrays. In this thesis, the author investi- gates the parameter deviation and non-idealities encountered during the development of a novel MTJ stack con guration. The characterization result provides motivation for the development of the design for testability (DFT) scheme that can help test and characterize STT-MRAM bit-cells and the CMOS peripheral circuitry e ciently. The primary factors for wafer yield degradation are the device parameter variation and its non-uniformity across the wafer due to the fabrication process non-idealities. There- fore, e ective in-process testing strategies for exploring and verifying the impact of the parameter variation on the wafer yield will be needed to achieve fabrication process opti- mization. While yield depends on the CMOS process variability, quality of the deposited MTJ lm, and other process non-idealities, test platform can enable parametric optimiza- tion and veri cation using the CMOS-based DFT circuits. In this work, we develop a DFT algorithm and implement a DFT circuit for parametric testing and prequali cation of the critical circuits in the CMOS wafer. The DFT circuit successfully replicates the electrical characteristics of MTJ devices and captures their spatial variation across the wafer with an error of less than 4%. We estimate the yield of the read sensing path by implement- ing the DFT circuit, which can replicate the resistance-area product variation up to 50% from its nominal value. The yield data from the read sensing path at di erent wafer loca- tions are analyzed, and a usable wafer radius has been estimated. Our DFT scheme can provide quantitative feedback based on in-die measurement, enabling fabrication process optimization through iterative estimation and veri cation of the calibrated parameters. Another concern that prevents mass production of STT-MRAM arrays is the defect formation in MTJ devices due to aging. Identifying manufacturing defects in the magnetic tunnel junction (MTJ) device is crucial for the yield and reliability of spin-torque-transfer (STT) magnetic random-access memory (MRAM) arrays. Several of the MTJ defects result in parametric deviations of the device that deteriorate over time. We extend our work on the DFT scheme by monitoring the electrical parameter deviations occurring due to the defect formation over time. A programmable DFT scheme was implemented for a sub-arrayin 65 nm CMOS technology to evaluate the feasibility of the test scheme. The scheme utilizes the read sense path to compare the bit-cell electrical parameters against known DFT cells characteristics. Built-in-self-test (BIST) methodology is utilized to trigger the onset of the fault once the device parameter crosses a threshold value. We demonstrate the operation and evaluate the accuracy of detection with the proposed scheme. The DFT scheme can be exploited for monitoring aging defects, modeling their behavior and optimization of the fabrication process. DFT scheme could potentially nd numerous applications for parametric characteriza- tion and fault monitoring of STT-MRAM bit-cell arrays during mass production. Some of the applications include a) Fabrication process feedback to improve wafer turnaround time, b) STT-MRAM bit-cell health monitoring, c) Decoupled characterization of the CMOS pe- ripheral circuitry such as read-sensing path and sense ampli er characterization within the STT-MRAM array. Additionally, the DFT scheme has potential applications for detec- tion of fault formation that could be utilized for deploying redundancy schemes, providing a graceful degradation in MTJ-based bit-cell array due to aging of the device, and also providing feedback to improve the fabrication process and yield learning
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