[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Log in

60 pW 20 μm size CMOS implementation of an actual soma membrane

  • Published:
Journal of Computational Electronics Aims and scope Submit manuscript

Abstract

The article presents a 65 nm technology implementation of a low-power artificial spiking neuron intellectual property (IP) core. The concept of the circuit is based on modeling the mechanism of a perikaryon membrane and the propagation of the action potential in an axon. Nerve impulses are generated using a current-controlled oscillator. Thanks to this oscillator, the circuit is implemented exclusively using metal-oxide semiconductor transistors, with the omission of capacitors. The main advantage of the presented implementation is the possibility of implementing the IP-core using nanometer digital complementary metal-oxide semiconductor technologies with the possibility of placing it within a common substrate with a digital processor. The circuit operates in weak-inversion mode with a 0.3 V power supply and does not require additional polarization voltages. The maximum frequency of impulse generation equals 2.5 kHz. The paper describes two operating modes of the circuit: a cortical mode with power consumption of 60 pW and a processing mode with power consumption of 52.2 pW. The power reduction was obtained using a circuit structure in which the power supply current consumption is reduced with the moment the circuit starts processing. The article also contains results of a process–voltage–temperature analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Davis, M., et al.: Loihi, neuromorphic manycore processor with on-chip learning. IEEE Micro 38(1), 82–99 (2018)

    Article  Google Scholar 

  2. Cheng, H.-P. et al.: Understanding the design of IBM neurosynaptic system and its tradeoffs: a user perspective. In: Design, Automation & Test in Europe Conference & Exhibition. IEEE (2017)

  3. Qualcomm: Zeroth cognitive processor. https://www.qualcomm.com/news/onq/2013/10/10/introducing-qualcomm-zeroth-processors-brain-inspired-computing (2013). Accessed 17 July 2019

  4. Basu, A., Hasler, P.E.: Nullcline-based design of a silicon neuron. IEEE Trans. Circuits Syst. I Regul. Pap. 57, 2938–2947 (2010)

    Article  MathSciNet  Google Scholar 

  5. Wu, X., Saxena, V., Zhu, K., Balagopal, S.: A CMOS Spiking Neuron for brain-inspired neural networks with resistive synapses and in-situ learning. IEEE Trans. Circuits Syst. II Express Br. 62(11), 1088–1092 (2015)

    Article  Google Scholar 

  6. Brandolini, M., et al.: A 5 GS/s 150 mW 10 b SHA-less pipelined/SAR hybrid ADC for direct-sampling systems in 28 nm CMOS. IEEE J. Solid State Circuits 50(12), 2922–2934 (2015)

    Article  Google Scholar 

  7. Mahmoud, S.A.: Digitally controlled CMOS balanced output transconductor and application to variable gain amplifier and Gm-C filter on field programmable analog array. J. Circuits Syst. Comput. 14(4), 667–684 (2005)

    Article  Google Scholar 

  8. Talaśka, T., Kolasa, M., Długosz, R., Pedrycz, W.: Analog programmable distance calculation circuit for winner takes all neural network realized in the CMOS technology. IEEE Trans. Neural Netw. Learn. Syst. 27(3), 661–673 (2016)

    Article  MathSciNet  Google Scholar 

  9. Szczęsny, S.: Current-mode FPAA with CMRR elimination and low sensitivity to mismatch. Circuits Syst. Signal Process. 36(7), 2672–2696 (2017)

    Article  Google Scholar 

  10. Gianni, C., Pennisi, S., Scotti, G., Trifiletti, A.: The universal circuit simulator: a mixed-signal approach to n-port network and impedance synthesis. IEEE Trans. Circuits Syst. I Regul. Pap. 54(10), 2178–2183 (2007)

    Article  MathSciNet  Google Scholar 

  11. Szczęsny, S.: HDL-Based Synthesis System with Debugger for Current-Mode FPAA. IEEE Trans. Comput. Aided Des. Integrated Circuits Syst. 37(5), 915–926 (2018)

    Google Scholar 

  12. Makiyama, H., Horita, K., Iwamatsu, T., Oda, H., Sugii, N., Inoue, Y., Yamamoto, Y.: Design consideration of 0.4 V-operation SOTB MOSFET for super low power application. In: International Meeting for Future of Electron Devices. IEEE (2011)

  13. Szczęsny, S.: 0.3 V 2.5 nW per channel current-mode CMOS perceptron for biomedical signal processing in amperometry. IEEE Sens. J. 17(17), 5399–5409 (2017)

    Article  Google Scholar 

  14. Kumar, A., Tripathi, M.M., Chaujar, R.: Ultralow-power dielectric-modulated nanogap-embedded sun-20-nm TGRC-MOSFET for biosensing applications. J. Comput. Electron. 17(4), 1807–1815 (2018)

    Article  Google Scholar 

  15. Chatterjee, S., Pun, K.P., Nebojsa, S., Tsividis, Y., Kinget, P.: Analog Circuit Design Techniques at 0.5 V. Analog Circuits and Signal Processing. Springer US (2007). https://doi.org/10.1007/978-0-387-69954-7

  16. Szczęsny, S.: High speed and low sensitive current-mode CMOS perceptron. Microelectron. Eng. 165, 41–51 (2016)

    Article  Google Scholar 

  17. Handkiewicz, A., Szczęsny, S., Kropidłowski, M.: Over rail-to-rail fully differential voltage-to-current converters for nm scale CMOS technology. Analog Integr. Circuits Signal Process. 94(1), 139–146 (2018)

    Article  Google Scholar 

  18. Sen, S., Venkataramani, S., Rughunathan, A.: Approximate computing for spiking neural networks. In: Design, Automation & Test in Europe Conference & Exhibition, pp. 193–198 (2017)

  19. Berger, T.W., Song, S., Chan, R.H.M.: A hippocampal cognitive prosthesis: multi-input, multi-output nonlinear modeling and VLSI implementation. IEEE Trans. Neural Syst. Rehabil. Eng. 20(2), 198–211 (2012)

    Article  Google Scholar 

  20. Huderek, D., Szczęsny, S., Rato, R.: Spiking neural network based on cusp catastrophe theory. Found. Comput. Dec. Sci. 44(3), 273–284 (2019)

    Google Scholar 

  21. Ferrari, G., et al.: Ultra-low-noise CMOS current preamplifier from DC to 1 MHz. Electron. Lett. 45(25), 1278–1280 (2009)

    Article  Google Scholar 

  22. Puglisi, F.M., Pavan, P.: Guidelines for a reliable analysis of random telegraph noise in electronic devices. IEEE Trans. Instrum. Meas. 65(6), 1435–1442 (2016)

    Article  Google Scholar 

  23. Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117(4), 500–544 (1952)

    Article  Google Scholar 

  24. Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Netw. 14(6), 1569–1572 (2003)

    Article  MathSciNet  Google Scholar 

  25. Vasallo, B.G., Mateos, J., González, T.: Ion shot noise in Hodgkin–Huxley neurons. J. Comput. Electron. 17(4), 1790–1796 (2018)

    Article  Google Scholar 

  26. Basu, A.: Small-signal neural models and their applications. IEEE Trans. Biomed. Circuits Syst. 6(1), 64–75 (2012)

    Article  Google Scholar 

  27. Laifi, A., Al Abaji, M.A., Thewes, R.: A 96 dB SNDR current-mode continuous-time-modulator for electrochemical sensor arrays. In: IEEE Mixed Design of Integrated Circuits & Systems (MIXDES), International Conference (2015)

  28. Standen, N.B.: Calcium and sodium ions as charge carriers in the action potential of an identified snail neurone. J. Physiol. 249(2), 241–252 (1975)

    Article  Google Scholar 

  29. Sun, L., Liu, H.: Steady state membrane potential and sodium current changes during high frequency electrical nerve stimulation. In: IEEE Seventh International Conference on Measuring Technology and Mechatronics Automation, pp. 1005–1008 (2015)

  30. Śniatała, P., Naumowicz, M., Handkiewicz, A., Szczęsny, S., De Melo, Joao L.A., Paulino, N., Goes, J.: Current mode sigma-delta modulator designed with the help of transistor’s size optimization tool. Bull. Pol. Acad. Sci. Tech. Sci. 63(4), 919–922 (2015)

    Google Scholar 

  31. Joubert, A., Belhadj, B., Temam, O., Heliot, R.: Hardware spiking neurons design: analog or digital?. In: The 2012 International Joint Conference on Neural Networks (2012)

  32. Sourikopoulos, I., Hedayat, S., Loyez, C., Danneville, F., Hoel, V., Mercier, E., Cappy, A.: A 4-fJ/spike artificial neuron in 65 nm CMOS technology. Front. Neurosci. (2017). https://doi.org/10.3389/fnins.2017.00123

    Article  Google Scholar 

  33. Markram, H., Gerstner, W., Sjöström, P.: Spike-timing-dependent plasticity: a comprehensive overview. Front. Synaptic Neurosci. 4(2), 1–3 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Szymon Szczęsny.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Szczęsny, S., Huderek, D. 60 pW 20 μm size CMOS implementation of an actual soma membrane. J Comput Electron 19, 242–252 (2020). https://doi.org/10.1007/s10825-019-01431-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10825-019-01431-2

Keywords

Navigation