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Hardware Specialization in Low-power Sensing Applications to Address Energy and Resilience

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Abstract

This paper explores implications of introducing machine learning capabilities within a hardware-specialized platform for low power embedded sensing applications. Such a platform enables algorithms well suited for analyzing complex sensor signals under strict energy constraints. However, the benefits go further, enabling the effects of errors to be overcome in the presence of hardware faults within the platform. Although errors can result in substantial bit-level perturbations, the approach described views these an alteration on the way that information is encoded within the embedded data. The new information encoding can thus be learned in the form of an error-aware model. The energy implications of hardware-specialized machine-learning kernels are analyzed using a fabricated custom IC, and the hardware-resilience implications are analyzed using an FPGA platform, which permits controllable and randomized injection of logical hardwarefaults.

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References

  1. Dubey, P. (2005). Recognition, mining and synthesis moves computers to the era of tera. Technology Intel Magazine, 9(2), 1–10.

    Google Scholar 

  2. Verma, N., Shoeb, A., Bohorquez, J., Dawson, J., Guttag, J., Chandrakasan, A.P. (2010). A micro-power EEG acquisition SoC with integrated feature extraction processor for a chronic seizure detection system. IEEE Journal of Solid-State Circuits, 45(4), 804–816.

    Article  Google Scholar 

  3. Lee, K.H., & Verma, N. (2012). A 1.2–0.55 V general-purpose biomedical processor with configurable machine-learning accelerators for high-order, patient-adaptive monitoring. In Proceedings of the IEEE in ESSCIRC (ESSCIRC) (pp. 285–288).

  4. Fan, K., Kudlur, M., Dasika, G., Mahlke, S. (2009). Bridging the computation gap between programmable processors and hardwired accelerators. In IEEE 15th International Symposium on High Performance Computer Architecture, 2009. HPCA 2009 (pp. 313–322). IEEE.

  5. Manavski, S.A. (2007). CUDA compatible GPU as an efficient hardware accelerator for AES cryptography. In IEEE International Conference on Signal Processing and Communications, 2007. ICSPC 2007 (pp. 65–68). IEEE.

  6. Guerdoux-Jamet, P., & Lavenier, D. (1997). SAMBA: hardware accelerator for biological sequence comparison. Comput. Appl. Biosci.: CABIOS, 13(6), 609–615.

    Google Scholar 

  7. Lee, K., & Verma, N. (2013). A low-power processor with configurable embedded machine-learning accelerators for high-order and adaptive analysis of medical-sensor signals. IEEE Journal of Solid-State Circuits, 48(7), 1625–1637.

    Article  Google Scholar 

  8. Shi, X., Hsu, S., Chen, F., Hsu, M., Socha, R., Dusa, M. (2002). Understanding the forbidden pitch phenomenon and assist feature placement. In: Proceedings SPIE (vol. 4689, pp. 985–996).

  9. Bohr, M. (2009). The new era of scaling in an SoC world. In IEEE Solid-State Circuits Conference-Digest of Technical Papers, 2009. ISSCC 2009 (pp. 23–28). IEEE International.

  10. McPherson, J. W. (2006). Reliability challenges for 45nm and beyond. In Proceedings of the 43rd Annual Design Automation Conference (pp. 176–181). ACM.

  11. Zhang, J., Lin, A., Patil, N., Wei, H., Wei, L., Wong, H., Mitra, S. (2012). Robust digital VLSI using carbon nanotubes. IEEE Transaction on Computer-aided Design of Integrated Circuits and Systems, 31(4), 453–471.

    Article  Google Scholar 

  12. Shulaker, M.M., Hills, G., Patil, N., Wei, H., Chen, H.-Y., Wong, H.-S.P., Mitra, S. (2013). Carbon nanotube computer. Nature, 501(7468), 526–530.

    Article  Google Scholar 

  13. Chang, L., Choi, Y.-k., Ha, D., Ranade, P., Xiong, S., Bokor, J., Hu, C., King, T.-J. (2003). Extremely scaled silicon nano-CMOS devices. Proceedings of IEEE, 91(11), 1860–1873.

    Article  Google Scholar 

  14. Ribes, G., Rafik, M., Roy, D. (2007). Reliability issues for nano-scale CMOS dielectrics. Microelectronic Engineering, 84(9), 1910–1916.

    Article  Google Scholar 

  15. Henkel, J., Bauer, L., Dutt, N., Gupta, P., Nassif, S., Shafique, M., Tahoori, M., Wehn, N. (2013). Reliable on-chip systems in the nano-era: lessons learnt and future trends. In: Proceedings of the 50th Annual Design Automation Conference (p. 99). ACM.

  16. Leem, L., Cho, H., Bau, J., Jacobson, Q. A., Mitra, S. (2010). ERSA: Error resilient system architecture for probabilistic applications. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2010 (pp. 1560–1565). IEEE.

  17. Shanbhag, N. (2002). Reliable and energy-efficient digital signal processing. In: Proceedings of the 39th annual Design Automation Conference (pp. 830–835). ACM.

  18. Ernst, D., Kim, N. S., Das, S., Pant, S., Rao, R., Pham, T., Ziesler, C., Blaauw, D., Austin, T., Flautner, K., et al. (2003).

  19. Shanbhag, N.R., Mitra, S., Veciana, G.d., Orshansky, M., Marculescu, R., Roychowdhury, J., Jones, D., Rabaey, J.M. (2008). The search for alternative computational paradigms. IEEE Design and Test of Computers, 25(4), 334–343.

    Article  Google Scholar 

  20. Shanbhag, N.R., Abdallah, R.A., Kumar, R., Jones, D.L. (2010). Stochastic computation. In: Proceedings of the 47th Design Automation Conference (pp. 859–864). ACM.

  21. Verma, N., Lee, K.H., Jang, K.J., Shoeb, A. (2012). Enabling system-level platform resilience through embedded data-driven inference capabilities in electronic devices,” In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012 (pp. 5285–5288). IEEE.

  22. Wang, Z., Lee, K.H., Verma, N. Overcoming computational errors in low-power sensing platforms through embedded machine-learning kernels. To appear on IEEE Transactions on Very Large Scale Integration (VLSI) systems.

  23. Chippa, V.K., Mohapatra, D., Raghunathan, A., Roy, K., Chakradhar, S.T. (2010). Scalable effort hardware design: exploiting algorithmic resilience for energy efficiency. In: Proceedings of the 47th Design Automation Conference (pp. 555–560). ACM.

  24. Sze, V., & Chandrakasan, A.P. (2011). A highly parallel and scalable CABAC decoder for next generation video coding. In IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC), 2011 (pp. 126–128). IEEE.

  25. Owens, J.D., Houston, M., Luebke, D., Green, S., Stone, J.E., Phillips, J.C. (2008). GPU computing. Proceedings of IEEE, 96(5), 879–899.

    Article  Google Scholar 

  26. Kwong, J., & Chandrakasan, A.P. (2011). An energy-efficient biomedical signal processing platform. IEEE Journal of Solid-State Circuits, 46(7), 1742–1753.

    Article  Google Scholar 

  27. Sun, F., Ravi, S., Raghunathan, A., Jha, N.K. (2004). Custom-instruction synthesis for extensible-processor platforms, (Vol. 23.

  28. Dusa, M., Quaedackers, J., Larsen, O.F., Meessen, J., van der Heijden, E., Dicker, G., Wismans, O., de Haas, P., van Ingen Schenau, K., Finders, J., et al. (2007). Pitch doubling through dual-patterning lithography challenges in integration and litho budgets. In Advanced Lithography. International Society for Optics and Photonics (pp. 65200G–65200G).

  29. Mansfield, S.M., Liebmann, L.W., Molless, A.F., Wong, A.K. (2000). Lithographic comparison of assist feature design strategies. In Microlithography 2000. International Society for Optics and Photonics (pp. 63–76).

  30. Horowitz, M., Alon, E., Patil, D., Naffziger, S., Kumar, R., Bernstein, K. (2005). Scaling, power, and the future of CMOS. In Electron Devices Meeting, 2005. IEDM Technical Digest. IEEE International. (pp. 7–15). IEEE.

  31. Austin, T., Bertacco, V., Blaauw, D., Mudge, T. (2005). Opportunities and challenges for better than worst-case design. In Proceedings of the 2005 Asia and South Pacific Design Automation Conference (pp. 2–7). ACM.

  32. Shooman, M.L. (2002). N-modular redundancy. Reliability of Computer Systems and Networks: Fault Tolerance, Analysis, and Design (pp. 145–201).

  33. Kim, E.P., & Shanbhag, N.R. (2012). Soft n-modular redundancy. IEEE Transactions on Computers, 61 (3), 323–336.

    Article  MathSciNet  Google Scholar 

  34. Hegde, R., & Shanbhag, N.R. (1999). Energy-efficient signal processing via algorithmic noise-tolerance. In Proceedings of the 1999 international symposium on Low power electronics and design (pp. 30–35). ACM.

  35. Hedge, R., & Shanbhag, N.R. (2001). Soft digital signal processing.

  36. Pillai, P., & Shin, K.G. (2001). Real-time dynamic voltage scaling for low-power embedded operating systems. In ACM SIGOPS Operating Systems Review (vol. 35(5), pp. 89– 102). ACM.

  37. Maglaveras, N., Stamkopoulos, T., Diamantaras, K., Pappas, C., Strintzis, M. (1998). ECG pattern recognition and classification using non-linear transformations and neural networks: a review. International Journal of Medical Informatics, 52(1), 191–208.

    Article  Google Scholar 

  38. Lee, K.H., Kung, S.-Y., Verma, N. (2011). Improving kernel-energy trade-offs for machine learning in implantable and wearable biomedical applications. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011 (pp. 1597–1600). IEEE.

  39. Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E. (2000). Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals.

  40. Shoeb, A.H. (2009). Application of machine learning to epileptic seizure onset detection and treatment. Ph.D. dissertation, Massachusetts Institute of Technology.

  41. Shih, E., & Guttag, J. (2008). Reducing energy consumption of multi-channel mobile medical monitoring algorithms. In Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments (p. 15). ACM.

  42. Shoeb, A.H., & Guttag, J.V. (2010). Application of machine learning to epileptic seizure detection. In Proceedings of the 27th International Conference on Machine Learning (ICML-10) (pp. 975–982).

  43. Übeyli, E.D. (2007). ECG beats classification using multiclass support vector machines with error correcting output codes. Digital Signal Processing, 17(3), 675–684.

    Article  Google Scholar 

  44. Wang, Z., & Verma, N. (2014). Error adaptive classifier boosting (EACB): exploiting data-driven training for highly fault-tolerant hardware. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014.

  45. Schertz, D.R., & Metze, G. (1972). A new representation for faults in combinational digital circuits. IEEE Transactions on Computers, 100(8), 858–866.

    Article  Google Scholar 

  46. Rabaey, J.M., Chandrakasan, A.P., Nikolic, B. (2002). Digital integrated circuits (vol. 2). Englewood Cliffs: Prentice hall.

    Google Scholar 

  47. Gallager, R.G. (2008). Principles of digital communication (vol. 1). Cambridge: Cambridge University Press.

    Book  Google Scholar 

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Acknowledgments

Support is provided by SRC, NSF (CCF-1253670), as well as Center for Future Architectures Research (C-FAR) and Systems on Nanoscale Information fabriCs (SONIC), two of the six SRC STARnet Centers, sponsored by MARCO and DARPA.

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Author notes

  1. Kyong Ho Lee

    Present address: Samsung Research America, Dallas, TX, USA

Authors and Affiliations

  1. Department of Electrical Engineering, Princeton University, Princeton, NJ, 08544, USA

    Zhuo Wang, Kyong Ho Lee & Naveen Verma

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  1. Zhuo Wang
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  2. Kyong Ho Lee
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  3. Naveen Verma
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Correspondence to Zhuo Wang.

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Wang, Z., Lee, K.H. & Verma, N. Hardware Specialization in Low-power Sensing Applications to Address Energy and Resilience. J Sign Process Syst 78, 49–62 (2015). https://doi.org/10.1007/s11265-014-0931-y

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  • DOI: https://doi.org/10.1007/s11265-014-0931-y

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