EECS 507 Papers

Lectures Piazza Papers Description Times Schedule Canvas References

General Information

Graded Paper Assignments

  1. 13 Sep.: L. Yang, R. P. Dick, H. Lekatsas, and S. Chakradhar, “High-performance operating system controlled on-line memory compression,” ACM Trans. Embedded Computing Systems, vol. 9, no. 4, pp. 30:1-30:28, Mar. 2010.
  2. 15 Sep.: R. Banakar, S. Steinke, B.-S. Lee, M. Balakrishnan, and P. Marwedel, “Scratchpad Memory: A Design Alternative for Cache On-chip Memory in Embedded Systems,” in Proc. Int. Conf. Hardware-Software Co-Design and System Synthesis, May 2002.
  3. 20 Sep.: A. Bonde, J. R. Codling, K. Naruethep, Y. Dong, W. Siripaktanakon, S. Ariyadech, A. Sangpetch, O. Sangpetch, S. Pan, H. Y. Noh, and P. Zhang, “PigNet: Failure-Tolerant Pig Activity Monitoring System Using Structural Vibration,” in Proc. Int. Symp. Information Processing in Sensor Networks, May 2021.
  4. 22 Sep.: C. L. Liu and James W. Layland, “Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment,” in J. ACM, vol. 20, no. 1, Jan. 1973, pp. 46–61.
  5. 27 Sep.: L. Zhang, B. Tiwana, Z. Qian, Z. Wang, R. P. Dick, Z. M. Mao, and L. Yang, “Accurate online power estimation and automatic battery behavior based power model generation for smartphones,” in Proc. Int. Conf. Hardware/Software Codesign and System Synthesis, Oct. 2010, pp. 105–114. Notes: On-line component-specific power estimation for smartphones, including a battery state based model construction technique. One of five most cited articles in the history of the Embedded System Week federation of conferences as of 2013.
  6. 29 Sep. (finish on 4 Oct): J. Polastre, R. Szewczyk, A. Mainwaring, D. Culler, and J. Anderson, “Analysis of wireless sensor networks for habitat monitoring,” in Wireless Sensor Networks, 2004, Springer.
  7. 4 Oct. (finish on 6 Oct.): U. Raza, P. Kulkarni, and M. Sooriyabandara, “Low power wide area networks: an overview,” in IEEE Communications Surveys and Tutorials, vol. 19, no. 2, 2017.
  8. 11 Oct.: E. Ronen, A. Shamir, A.-O Weingarten, C. O'Flynn, “IoT goes nuclear: creating a ZigBee chain reaction,” in Proc. Symp. on Security and Privacy, May 2017.
  9. 8 Nov.: B. Widrow and M. A. Lehr, “30 years of adaptive neural networks: perceptron, madaline, and backpropagation,” in Proc. IEEE, vol. 78, no. 9, Sep. 1990.
  10. 10 Nov.: Y. Zhu, A. Samajdar, M. Mattina, P. Whatmough, “Euphrates: algorithm-SoC co-design for low-power mobile continuous vision,” arXiv, Apr. 2018.
  11. 15 Nov.: M. Zhu, T. Zhang, Z. Gu, and Y. Xie, “Sparse Tensor Core: Algorithm and Hardware Co-Design for Vector-wise Sparse Neural Networks on Modern GPUs,” Proc. Int. Symp. Microarchitecture, Oct. 2019.
  12. 17 Nov.: M. A. Zidan, J. P. Strachan, and W. D. Lu, “The future of electronics based on memristive systems,” in Nature Electronics, vol. 1, Jan. 2018.
  13. 29 Nov.: P. Kanerva, “Hyperdimensional computing: an introduction to computing in distributed representation with high-dimensional random vectors,” Cognitive Computing, vol. 1, Jan. 2009. The summary only covers the Abstract, Hyperdimensional Computing, and Constructing a Cognitive Code sections, which amount to nine pages.
  14. 1 Dec.: M. Chen, D. Gündüz, K. Huang, W. Saad, M. Bennis, A. V. Feljan, and H. V. Poor, “Distributed learning in wireless networks: recent progress and future challenges,” IEEE J. on Selected Areas in Communication, vol. 39, no. 12, Dec. 2021.

Paper Assignments Without Student Presentations

  1. 6 Sep.: A. Jantsch and I. Sander, “Models of computation and languages for embedded system design,” IEE Proc., pp. 114–129, 2005. Unscored but completion is required.
  2. 8 Sep.: R. P. Dick, “Multiobjective synthesis of low-power real-time distributed embedded systems,” Ph.D. dissertation, Dept. of Electrical Engineering, Princeton University, July 2002. Read Chapter 4. No need to write a summary for this one.
  3. 6 Oct.: R. P. Dick, R. Aitken, J. Mogill, J. P. Strachen, K. Bresniker, W. Lu, Y. Nakahira, Z. Li, M. J. Marinella, W. Severa, A. Talin, C. Vineyard, S. Kumar, C. Mailhiot, and L. Klebanoff, “Research Challenges for Energy-Efficient Computing in Automated Vehicles,” to appear in IEEE Computer.
  4. 20 Oct.: R. P. Dick, L. Shang, M. Wolf, and S.-W. Yang, “Embedded intelligence in the Internet-of-Things,” IEEE Design & Test of Computers, Dec. 2019.

References For Those Interested in Greater Depth

  1. S. A. Edwards, “Design and verification languages,” Columbia University, Tech. Rep., Nov. 2004.
  2. Introduction of Michael R. Garey and David S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman & Company, NY, 1979.
  3. O. Coudert, “Exact Coloring of Real-Life Graphs is Easy,” in Proc. Design Automation Conf., Jun. 1997, pp. 121–126.
  4. R. I. Davis and A. Burns, “A survey of hard real-time scheduling for multiprocessor systems,” in ACM Computing Surveys, vol. 43, no. 4, Oct. 2011.
  5. Robert P. Dick, “Reliability, Thermal, and Power Modeling and Optimization,” in Proc. Int. Conf. on Computer-Aided Design, 2010, Nov and D. S. Khudia, G. Wright, and S. Mahlke, “Efficient soft error protection for commodity embedded microprocessors using profile information,” ACM SIGPLAN, vol. 26, no. 5, pp. 99–108, Oct. 2012.
  6. Jonathan Savage, “Stryd footpod review,” last updated 19 Mar. 2020, blog post. I posted this because a student was curious about the relative performance of GPS and IMUs for velocity estimation. The answer is that IMUs do very, very well when supported by the right signal processing algorithms, but those algorithms are non-trivial.
  7. E. Genç, C. Fraenz, C. Schlüter, P. Friedrich, R. Hossiep, M. C. Voelkle, J. M. Ling, O. Güntürkün, and R. E. Jung, “Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence,” Nature Communications, vol. 9, 2018.
  8. J. J. Atick and A. N. Redlich, “Toward a theory of early visual processing,” Neural Computation, vol. 2, pp. 308–320, 1990.
  9. J. E. Niven and S. B. Laughlin, “Energy limitation as a selective pressure on the evolution of sensory systems,” J. Experimental Biology, vol. 211, pp. 1792–1804, Apr. 2008.
  10. S. Li, J. Jiao, Y. Han, and T. Weissman, “Demystifying ResNet,” arXiv 1611.01186, 2016.
  11. Y. LeCun, “A theoretical framework for back-propagation,” in Proc. Connectionist Models Summer School, 1988, pp. 21–28.
  12. A. G. Anderson and C. P. Berg, “The high-dimensionality geometry of binary neural networks,”Proc. Int. Conf. on Learning Representations, May 2017.
  13. H. Li, Z. Xu, G. Taylor, C. Studer, and T. Goldstein, “Visualing the Loss Landscape of Neural Nets,” in Proc. Conf. on Neural Information Processing Systems, Dec. 2018.

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