Lecture notes on Embedded system Design

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Embedded Systems 1. Introduction Lothar Thiele Swiss Federal Computer Engineering 1 - 1 Institute of Technology and Networks Laboratory Organization WWW: http://www.tik.ee.ethz.ch/tik/education/lectures/ES/ Lecture: Lothar Thiele, thieleethz.ch Coordination: Rehan Ahmed, rehan.ahmedtik.ee.ethz.ch References:  P. Marwedel: Embedded System Design (paperback), Springer Verlag, December 2011, ISBN: 978-94-007-0256-1.  G.C. Buttazzo: Hard Real-Time Computing Systems. Springer Verlag, 2011.  W. Wolf: Computers as Components – Principles of Embedded System Design. Morgan Kaufman Publishers, 2012.  J. Teich: Digitale Hardware/Software Systeme, Springer Verlag, 2007. The slides contain material of J. Rabaey, K. Keuzer, Wayne Wolf, Peter Marwedel, Philip Koopman and from the above books of J. Teich, G.C. Buttazzo, W. Wolf and P. Marwedel. Swiss Federal Computer Engineering 1 - 2 Institute of Technology and Networks Laboratory Communicating Embedded Systems Example: BTnodes  complete platform including OS  especially suited for pervasive computing applications, internet of things Sensor Actuator Swiss Federal Computer Engineering 1 - 3 Institute of Technology and Networks Laboratory BTnode Platform Communication via generic platform Bluetooth for wireless Data Transceiver distributed Interfaces embedded computing complete platform including OS especially suited for pervasive computing applications nd 2 Radio Microprocessor (IoT) and Memory Batteries Swiss Federal Computer Engineering 1 - 4 Institute of Technology and Networks Laboratory Where are sensor nodes used? Factory Automation Logistics Maintenance Low resolution Sensor, Test4, Increasing frequency 1 0.5 0 -0.5 -1 Natural Hazards 0 2 4 6 8 10 12 14 16 18 Time (sec) Building Automation Health Care Swiss Federal Computer Engineering 1 - 5 Institute of Technology and Networks Laboratory Acceleration (g)PermaSense Project Jan Beutel – ETH Zurich Swiss Federal Computer Engineering 1 - 6 Institute of Technology and Networks Laboratory Swiss Federal Computer Engineering 1 - 7 Institute of Technology and Networks Laboratory Development in ES Exercise Linux GNU GCC AVR libc Eclipse BTNut OS Swiss Federal Computer Engineering 1 - 8 Institute of Technology and Networks Laboratory Contents of Course 1. Embedded Systems Introduction 2. Software Introduction 7. System Components 10. Models 11. Architecture 3. Real-Time Models 8. Communication Synthesis 4. Periodic/Aperiodic Tasks 9. Low Power Design 5. Resource Sharing 6. Real-Time OS 12. Model Based Design Software and Processing and Hardware Programming Communication Swiss Federal Computer Engineering 1 - 9 Institute of Technology and Networks Laboratory Contents of Course 1. Embedded Systems Introduction 2. Software Introduction 7. System Components 10. Models 11. Architecture 3. Real-Time Models 8. Communication Synthesis 4. Periodic/Aperiodic Tasks 9. Low Power Design 5. Resource Sharing 6. Real-Time OS 12. Model Based Design Software and Processing and Hardware Programming Communication Swiss Federal Computer Engineering 1 - 10 Institute of Technology and Networks Laboratory 1 Embedded Systems Embedded systems (ES) = information processing systems embedded into a larger product Examples: Main reason for buying often is not information processing Swiss Federal Computer Engineering 1 - 11 Institute of Technology and Networks Laboratory Embedded Systems external process human interface sensors, actuators embedded system Swiss Federal Computer Engineering 1 - 12 Institute of Technology and Networks Laboratory Examples of Embedded Systems Car as an integrated control-, communication and information system. information gear box ABS motor control climate control Swiss Federal Computer Engineering 1 - 13 Institute of Technology and Networks Laboratory Examples of Embedded Systems Consumer electronics, for example MP3 Audio, digital camera, home electronics, … . user interface processor sensors actuators Swiss Federal Computer Engineering 1 - 14 Institute of Technology and Networks Laboratory Examples of Embedded Systems Production systems Swiss Federal Computer Engineering 1 - 15 Institute of Technology and Networks Laboratory Sometimes denoted as: Smart World cyber-physical system Computation (CPS) reasoning deciding Communication Hardware & CYBER Software WORLD observing influencing PHYSICAL Nature WORLD physical/biological/social processes Use feedback to influence the dynamics of the physical world by taking smart decisions in the cyber world Swiss Federal Computer Engineering 1 - 16 Institute of Technology and Networks Laboratory © http://www.satiztpm.it/ Internet of Things (IoT) infrastructure will be omnipresent. Swiss Federal Computer Engineering 1 - 17 Institute of Technology and Networks Laboratory Predictability & Dependability CPS = cyber-physical system “It is essential to predict how a CPS is going to behave under Maj14 any circumstances … before it is deployed.” “CPS must operate dependably, safely, securely, efficiently Raj10 and in real-time.” Maj14 R. Majumdar & B. Brandenburg (2014). Foundations of Cyber-Physical Systems. Raj10 R. Rajkumar et al. (2010). Cyber-Physical Systems: The Next Computing Revolution. Swiss Federal Computer Engineering 1 - 18 Institute of Technology and Networks Laboratory Characteristics of Embedded Systems (1) Must be dependable:  Reliability: R(t) = probability of system working correctly provided that it was working at t=0  Maintainability: M(d) = probability of system working correctly d time units after error occurred.  Availability: probability of system working at time t  Safety: no harm to be caused  Security: confidential and authentic communication Making the system dependable must not be an after-thought, it must be considered from the very beginning. Swiss Federal Computer Engineering 1 - 19 Institute of Technology and Networks Laboratory Characteristics of Embedded Systems (2) Must be efficient:  Energy efficient  Code-size and data memory efficient  Run-time efficient  Weight efficient  Cost efficient Dedicated towards a certain application: Knowledge about behavior at design time can be used to minimize resources and to maximize robustness. Swiss Federal Computer Engineering 1 - 20 Institute of Technology and Networks Laboratory

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