Introduction to Embedded Systems Research: Course...
Transcript of Introduction to Embedded Systems Research: Course...
Introduction to Embedded Systems Research:Course Review
Robert Dick
[email protected] of Electrical Engineering and Computer Science
University of Michigan
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OverviewPre-Midterm
Post-MidtermAdvising offer
Outline
1. Overview
2. Pre-Midterm
3. Post-Midterm
4. Advising offer
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OverviewPre-Midterm
Post-MidtermAdvising offer
What I am attempting to rate
Do you have a broad understanding of research topics and ideas connectedto embedded system analysis, design, and implementation?
Do you have a deep understanding of the challenges facing IoT systems andhow they relate to using machine learning techniques for analysis anddecision making?
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OverviewPre-Midterm
Post-MidtermAdvising offer
How I will rate these things
2/3–3/4 of questions will be on post-midterm topics.
Around 2/3 of questions will be high-level, e.g., did you understand the mainnew idea in a particular paper?
Around 1/3 of questions will be more detailed and may require somecalculation, although algebra will generally be sufficient for most of these.
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OverviewPre-Midterm
Post-MidtermAdvising offer
Placing out
I may conclude that there is sufficient evidence to assign grades to somestudents without a final exam.
If you are one of these students, I will contact you. You will have the optionto accept the proposed grade or take the final, in which case the final will beweighted in appropriately.
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How to study
Lectures
Review all the lecture notes.
Review video sections on topics you don’t remember well.
Papers
Do you understand the main new ideas in the paper?
Read your summaries of all papers.
Skim the summaries of two other students.
Use Piazza to discuss ambiguous concepts.
I will check Piazza frequently until the exam.
Projects
Goal, challenges, ideas, and results (did it work?).
Read your notes on student presentations, or read their slides.
Watch videos for topics that aren’t clearly explained in the slides.
OverviewPre-Midterm
Post-MidtermAdvising offer
Outline
1. Overview
2. Pre-Midterm
3. Post-Midterm
4. Advising offer
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OverviewPre-Midterm
Post-MidtermAdvising offer
Topics I
Application trends.
Technology trends.
Costs and constraints.
Specification languages and models.
Allocation, assignment, and scheduling.
Memory hierarchies.
Embedded and real-time operating systems.
Sensors and actuators.
Wireless power transfer applications.
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OverviewPre-Midterm
Post-MidtermAdvising offer
Topics II
Cyberphysical systems.
Energy- and temperature-aware low-power design and power modeling.
Wireless communication and its impact on power consumption.
Reliability-aware design and formal methods.
A little bit of material on testing.
Embedded system security.
Smartphones.
Wireless sensor networks.
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OverviewPre-Midterm
Post-MidtermAdvising offer
Outline
1. Overview
2. Pre-Midterm
3. Post-Midterm
4. Advising offer
10 R. Dick EECS 598-13
OverviewPre-Midterm
Post-MidtermAdvising offer
Topics I
Wearables.
Autonomous vehicles.
Embedded vision applications.
Communication, machine learning, and energy efficiency in theInternet-of-Things.
Energy-efficient machine learning algorithms: pruning, BNNs, weightcompression, etc.
Energy-efficient machine learning hardware.
LPWANs.
Devices for machine learning.
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OverviewPre-Midterm
Post-MidtermAdvising offer
Topics II
Reliability, security, and privacy in the Internet-of-Things.
Vision in the Internet-of-Things.
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OverviewPre-Midterm
Post-MidtermAdvising offer
Student projects
Evolving PCB antennas.
Sensing pollinators.
Analysis of Bluetooth mesh network.
Incentivizing message forwarding in delay-/defect-tolerant networks.
Modified base delta L1 cache compression.
Long-range LoRa.
Signal processing pipeline auto-calibration.
Scene cache for energy-efficient machine vision.
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OverviewPre-Midterm
Post-MidtermAdvising offer
Papers I
S. A. Edwards, “Design and verification languages,” Columbia University,Tech. Rep., Nov. 2004.
L. Yang, R. P. Dick, H. Lekatsas, and S. Chakradhar, “High-performanceoperating system controlled on-line memory compression,” ACM Trans.Embedded Computing Systems, vol. 9, no. 4, pp. 30:1–30:28, Mar. 2010.
R. I. Davis and A. Burns, “A survey of hard real-time scheduling formultiprocessor systems,” ACM Computing Surveys, vol. 43, no. 4, Oct. 2011.
E. A. Lee, “The past, present and future of cyber-physical systems: A focuson models,” Sensors, Feb. 2015.
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OverviewPre-Midterm
Post-MidtermAdvising offer
Papers II
A. P. Sample, D. J. Yeager, P. S. Powledge, A. V. Mamishev, and J. R.Smith, “Design of an rfid-based battery-free programmable sensingplatform,” IEEE Trans. on Instrumentation and Measurement, vol. 57,no. 11, pp. 2608–2615, Nov. 2008.
L. Zhang, B. Tiwana, Z. Qian, Z. Wang, R. P. Dick, Z. M. Mao, and L.Yang, “Accurate online power estimation and automatic battery behaviorbased power model generation for smartphones,” in Proc. Int. Conf.Hardware/Software Codesign and System Synthesis, Oct. 2010, pp. 105–114.
W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficientcommunication protocol for wireless microsensor networks,” in Proc. HawiiInt. Conf. on System Sciences, 2000.
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OverviewPre-Midterm
Post-MidtermAdvising offer
Papers III
D. Zhu, R. Melhem, and D. Mosse, “The effects of energy management onreliability in real-time embedded systems,” in Proc. Int. Conf.Computer-Aided Design, Nov. 2004.
T. Trippel, O. Weisse, W. Xu, P. Honeyman, and K. Fu, “WALNUT: Wagingdoubt on the integrity of MEMS accelerometers with acoustic injectionattacks,” in Proc. European Symp. on Security and Privacy, Apr. 2017.
J. Polastre, R. Szewczyk, A. Mainwaring, D. Culler, and J. Anderson,“Analysis of wireless sensor networks for habitat monitoring,” in WirelessSensor Networks, C. S. Raghavendra, K. M. Sivalingam, and T. Znati, Eds.Springer US, 2004, ch. 18, pp. 399–423.
D. Cruz, J. McClintock, B. Perteet, O. A. A. Orqueda, Y. Cao, andR. Fierro, “A multivehicle platform for research in networked embeddedsystems,” IEEE Control Systems Magazine, June 2007.
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OverviewPre-Midterm
Post-MidtermAdvising offer
Papers IV
C. Farabet, B. Martini, B. Corda, P. Akselrod, E. Culurciello, , andY. LeCun, “NeuFlow: A runtime reconfigurable dataflow processor forvision,” in Proc. Conf. Computer Vision and Pattern Recognition, June 2011.
B. Widrow and M. A. Lehr, “30 years of adaptive neural networks:Perceptron, madaline, and backpropagation,” Proc. IEEE, vol. 78, no. 9,Sept. 1990.
V. Sze, Y.-H. Chen, T.-J. Yang, and J. Emer, “Efficient processing of deepneural networks: A tutorial and survey,” Proc. IEEE, vol. 105, no. 12, Dec.2017.
S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally,“EIE: Efficient inference engine on compressed deep neural network,” inProc. Int. Symp. Computer Architecture, June 2016.
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OverviewPre-Midterm
Post-MidtermAdvising offer
Papers V
A. S. Cassidy et al., “Real-time scalable cortical computing at 46giga-synaptic OPS/Watt with 100x speedup in time-to-solution and100,000x reduction in energy-to-solution,” in Proc. Int. Conf. HighPerformance Computing, Networking, Storage and Analysis, Nov. 2014.
P. Coussy, C. Chavet, H. Wouafo, and L. Conde-Canecia, “Fully binaryneural network model and optimized hardware architectures for associativememories,” ACM J. on Emerging Technologies in Computing Systems,vol. 11, no. 4, Apr. 2015.
U. Raza, P. Kulkarni, and M. Sooriyabandara, “Low power wide areanetworks: An overview,” IEEE Communications Surveys and Tutorials,vol. 19, no. 2, May 2017.
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OverviewPre-Midterm
Post-MidtermAdvising offer
Papers VI
P. M. Sheridan, F. Cai, C. Du, W. Ma, Z. Zhang, and W. D. Lu, “Sparsecoding with memristor networks,” Nature Nanotechnology, vol. 12, Aug.2017.
E. Ronen, A. Shamir, A.-O. Weingarten, and C. O’Flynn, “IoT goes nuclear:Creating a ZigBee chain reaction,” in Proc. Symp. on Security and Privacy,May 2017.
Y. Zhu, A. Samajdar, M. Mattina, and P. Whatmough, “Euphrates:Algorithm-SoC co-design for low-power mobile continuous vision,” arXiv,Tech. Rep., Apr. 2018.
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OverviewPre-Midterm
Post-MidtermAdvising offer
Outline
1. Overview
2. Pre-Midterm
3. Post-Midterm
4. Advising offer
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OverviewPre-Midterm
Post-MidtermAdvising offer
Advising
Research, product design, or career options.
Group discussion.
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