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Introduction

  • Saurabh Jain
  • Longyang Lin
  • Massimo Alioto
Chapter
  • 34 Downloads

Abstract

This chapter opens the book and provides a brief analysis of historical trends in digital systems. Highlighting the gradual evolution of the semiconductor industry towards distributed computing (e.g., Internet of things—IoT), the demand for increasingly lower power in the common case and much higher peak performance is discussed. This has led to the introduction of various popular techniques such as wide dynamic voltage frequency scaling (DVFS). Then, the challenges posed by wide DVFS in both the clock and the data path are discussed, motivating the next chapters of the book.

Keywords

Dynamic voltage frequency scaling (DVFS) Distributed computing Internet of things (IoT) Machine learning Artificial intelligence (AI) Energy trends Cost trends Semiconductor market S-curve Gompertz function Power budget Microprocessor trends Graphics processing unit (GPU) Digital signal processor (DSP) Koomey’s law Gene’s law Supercomputer Short-range radios Energy per AD conversion Power-limited systems Battery-powered systems Carrier modulation Time-driven systems Event-driven systems Duty cycling Wake-up Always-on Latency Intelligent sensor nodes Speech recognition Smart buildings Applications Computer vision Audio monitoring Dynamic energy Leakage energy Activity factor Switched capacitance Supply voltage Leakage current Clock cycle Wire delay Gate delay Leakage/dynamic energy ratio Wire/gate delay ratio 

References

  1. 1.
    M. Alioto (ed.), Enabling the Internet of Things—From Integrated Circuits to Integrated Systems (Springer, Berlin, 2017)Google Scholar
  2. 2.
    W. Rhines, Gompertz predicts the future. (SemiWiki, 2019 [Online]), https://semiwiki.com/wally-rhines/273854-chapter-four-gompertz-predicts-the-future/. Accessed 2 Aug 2019
  3. 3.
    C.G. Bell, R. Chen, S. Rege, Effect of technology on near term computer structures. IEEE Comput. 5(2), 29–38 (1972)CrossRefGoogle Scholar
  4. 4.
    G. Bell, Bell’s law for rise and death of computer classes. Commun. ACM 51(1), 86–94 (2008)CrossRefGoogle Scholar
  5. 5.
    M. Fojtik, D. Kim, G. Chen, Y.-S. Lin, D. Fick, J. Park, M. Seok, M.-T. Chen, Z. Foo, D. Blaauw, D. Sylvester, A millimeter-scale energy-autonomous sensor system with stacked battery and solar cells. IEEE J. Solid State Circuits 48(3), 801–813 (2013)CrossRefGoogle Scholar
  6. 6.
    M. Alioto, V. De, A. Marongiu, Energy-quality scalable integrated circuits and systems: continuing energy scaling in the twilight of Moore’s law. IEEE J. Emerg. Select. Topics Circuits Syst. 8(4), 653–678 (2018)CrossRefGoogle Scholar
  7. 7.
    M. Alioto, Energy harvesters for IoT: applications and key aspects—short course at VLSI Symposium 2015, Kyoto (Japan). Accessed 15 June 2015Google Scholar
  8. 8.
  9. 9.
    M. Alioto, Ultra-low power VLSI circuit design demystified and explained: a tutorial. IEEE Trans. Circuits Syst. Pt. I (Invited) 59(1), 3–29 (2012)MathSciNetCrossRefGoogle Scholar
  10. 10.
    L. Camunas-Mesa, C. Zamarreno-Ramos, A. Linares-Barranco, A.J. Acosta-Jimenez, T. Serrano-Gotarredona, B. Linares-Barranco, An event-driven multi-kernel convolution processor module for event-driven vision sensors. IEEE J. Solid State Circuits 47(2), 504–517 (2012)CrossRefGoogle Scholar
  11. 11.
    M. Rusci, D. Rossi, M. Lecca, M. Gottardi, E. Farella, L. Benini, An event-driven ultra-low-power smart visual sensor. IEEE Sens. J. 16(13), 5344–5353 (2016)CrossRefGoogle Scholar
  12. 12.
    G. Singh, A. Nelson, S. Lu, R. Robucci, C. Patel, N. Banerjee, Event-driven low-power gesture recognition using differential capacitance. IEEE Sens. J. 16(12), 4955–4967 (2016)CrossRefGoogle Scholar
  13. 13.
    M. Price, J. Glass, A.P. Chandrakasan, A scalable speech recognizer with deep-neural-network acoustic models and voice-activated power gating, in IEEE International Solid-State Circuits Conference (ISSCC) Digest of Technical Papers, (2017), pp. 244–245Google Scholar
  14. 14.
    M. Yang, C. Chien, T. Delbruck, S. Liu, A 0.5 V 55μW 64x2-channel binaural silicon cochlea for event-driven stereo-audio sensing. IEEE J. Solid State Circuits 51(11), 2554–2569 (2016)CrossRefGoogle Scholar
  15. 15.
    S. Lecoq, J. Le Bellego, A. Gonzalez, B. Larras, A. Frappé, Low-complexity feature extraction unit for “Wake-on-Feature” speech processing, in Proceedings of ICECS, (2018), pp. 677–680Google Scholar
  16. 16.
    C. Weltin-Wu, Y. Tsividis, An event-driven clockless level-crossing ADC with signal-dependent adaptive resolution. IEEE J. Solid State Circuits 48(9), 2180–2190 (2013)CrossRefGoogle Scholar
  17. 17.
    M. Simonov, G. Chicco, G. Zanetto, Event-driven energy metering: principles and applications. IEEE Trans. Indus. Appl. 53(4), 3217–3227 (2017)CrossRefGoogle Scholar
  18. 18.
    Y. Xiao, W. Li, M. Siekkinen, P. Savolainen, A. Ylä-Jääski, P. Hui, Power management for wireless data transmission using complex event processing. IEEE Trans. Comput. 61(12), 1765–1777 (2012)MathSciNetCrossRefGoogle Scholar
  19. 19.
    X. Huang, P. Harpe, G. Dolmans, H. de Groot, J.R. Long, A 780–950 MHz, 64–146 μW power-scalable synchronized-switching OOK receiver for wireless event-driven applications. IEEE J. Solid State Circuits 49(5), 1135–1147 (2014)CrossRefGoogle Scholar
  20. 20.
    A. Chandrakasan, D. Daly, D. Finchelstein, J. Kwong, Y. Ramadass, M. Sinangil, V. Sze, N. Verma, Technologies for ultradynamic voltage scaling. Proc. IEEE 98(2), 191–214 (2010)CrossRefGoogle Scholar
  21. 21.
    H. Kaul, M.A. Anders, S.K. Mathew, S.K. Hsu, A. Agarwal, F. Sheikh, R.K. Krishnamurthy, S. Borkar, A 1.45GHz 52-to-162GFLOPS/W variable-precision floating-point fused multiply-add unit with certainty tracking in 32nm CMOS, in IEEE ISSCC Digest of Technical Papers, (2012), pp. 182–183Google Scholar
  22. 22.
    J. Myers, A. Savanth, D. Howard, R. Gaddh, P. Prabhat, D. Flynn, An 80nW retention 11.7pJ/cycle active sub-threshold ARM Cortex®-M0+ Sub-System in 65nm CMOS for WSN applications, in IEEE ISSCC Digest of Technical Papers, (2015), pp. 144–145Google Scholar
  23. 23.
    S. Hsu, A. Agarwal, M. Anders, S. Mathew, H. Kaul, F. Sheikh, R. Krishnamurthy, A 280mV-to-1.1V 256b reconfigurable SIMD vector permutation engine with 2-dimensional shuffle in 22nm CMOS, in ISSCC Digest of Technical Papers (ISSCC), San Francisco (CA), (2012)Google Scholar
  24. 24.
    J. Wang, N. Pinckney, D. Blaauw, D. Sylvester, Reconfigurable self-timed regenerators for wide-range voltage scaled interconnect. Proc. ASSCC 2015, 18–15 (2015)Google Scholar
  25. 25.
    S. Jain et al., A 280mV-to-1.2V wide-operating-range IA-32 processor in 32nm CMOS, in IEEE ISSCC Digest of Technical Papers, (2012), pp. 66–67Google Scholar
  26. 26.
    F. Sheikh, S. Mathew, M. Anders, H. Kaul, S. Hsu, A. Agarwal, R. Krishnamurthy, S. Borkar, A 2.05GVertices/s 151mW lighting accelerator for 3D graphics vertex and pixel shading in 32nm CMOS, in IEEE ISSCC Digest of Technical Papers, (2012), pp. 178–179Google Scholar
  27. 27.
    G. Gammie, N. Ickes, M. Sinangil, R. Rithe, J. Gu, A. Wang, H. Mair, S. Datla, R. Bing, S. Honnavara-Prasad, L. Ho, G. Baldwin, D. Buss, A. Chandrakasan, U. Ko, A 28nm 0.6V low-power DSP for mobile applications, in ISSCC Digest of Technical Papers (ISSCC), San Francisco (CA), (2011)Google Scholar
  28. 28.
    S. Jain, L. Lin, M. Alioto, Design-oriented energy models for wide voltage scaling down to the minimum energy point. IEEE Trans. CAS Pt. I (TCAS-I) 64(12), 3115–3125 (2017)Google Scholar
  29. 29.
    S. Jain, L. Lin, M. Alioto, Dynamically adaptable pipeline for energy-efficient microarchitectures under wide voltage scaling. IEEE J. Solid-State Circuits 53(2), 632–641 (2018)CrossRefGoogle Scholar
  30. 30.
    S. Jain, L. Lin, M. Alioto, Drop-in energy-performance range extension in microcontrollers beyond VDD scaling, in IEEE Asian Solid-State Circuits Conference (A-SSCC), (2019)Google Scholar
  31. 31.
    S. Jain, L. Lin, M. Alioto, Automated design of reconfigurable micro-architectures for accelerators under wide voltage scaling, in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, (2019), pp. 1–14Google Scholar
  32. 32.
    L. Lin, S. Jain, M. Alioto, A 595pW 14pJ/cycle microcontroller with dual-mode standard cells and self-startup for battery-indifferent distributed sensing, in IEEE ISSCC Digest of Technical Papers, (2018), pp. 44–45Google Scholar
  33. 33.
    L. Lin, S. Jain, M. Alioto, Integrated power management and microcontroller for ultra-wide power adaptation down to nW, in VLSI Symposium 2019, Kyoto (Japan), (2019), pp. C178–C179Google Scholar
  34. 34.
    L. Lin, S. Jain, M. Alioto, Reconfigurable clock networks for wide voltage scaling. IEEE J. Solid State Circuits 54(9), 2622–2631 (2019)CrossRefGoogle Scholar
  35. 35.
    L. Lin, S. Jain, M. Alioto, Reconfigurable clock networks for random skew mitigation from subthreshold to nominal voltage, in IEEE ISSCC Digest of Technical Papers, San Francisco (CA), (2017), pp. 440–441Google Scholar
  36. 36.
    S. Jain, M. Alioto, RECMICRO: design framework and scripts to design reconfigurable microarchitectures [Online], http://www.green-ic.org/recmicro.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Saurabh Jain
    • 1
  • Longyang Lin
    • 1
  • Massimo Alioto
    • 1
  1. 1.National University of SingaporeSingaporeSingapore

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