Basic Computations Using a Novel Scalable Pulse-Mode Modules

  • Thamira Hindo
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 247)


In this chapter the basic computational functions used in many algorithms are implemented in pulse mode. For this purpose, a novel circuit is proposed for pulse-based logarithmic computation using integrate-and-fire (IF) structures. The smallest unit in the module is a network of three IF units that implements a margin propagation (MP) function using integration and threshold operations inherited in the response of an IF neuron. The three units are connected together through excitatory and inhibitory inputs to impose constraints on the network firing-rate. The MP function is based on the log likelihood computation in which the multiplication of the inputs is translated into a simple addition. The advantage of using integrate-and-fire margin propagation (IFMP) is to implement a complex non-linear and dynamic programming functions of spike based (pulse based) computation in a modular and scalable way. In addition to scalability, the objective of the proposed module is to map algorithms into low power circuits as an attempt to implement signal processing applications on silicon. The chapter shows the mechanism of IFMP circuit, dynamic characteristics, the cascaded modularity, the verification of the algorithm in analog circuit using standard \(0.5 \upmu m\) CMOS technology and the basic functions computation.


Excitatory Inhibitory Integrate and fire log-sum-exp Margin propagation Pulse mode computation. 


  1. 1.
    Mead CA (1989) Analog VLSI and neural systems. Addison-Wesley, BostonGoogle Scholar
  2. 2.
    Mahowald M (1992) VLSI analogs of neuronal visual processing: a synthesis of form and function. Technical report. California Institute of Technology, PasadenaGoogle Scholar
  3. 3.
    Boahen K (2005) Neuromorphic microchips. Sci Am 292(5):56–63CrossRefGoogle Scholar
  4. 4.
    Liu S-C, Delbruck T (2010) Neuromorphic sensory systems. Curr Opin Neurobiol 20:1–8CrossRefGoogle Scholar
  5. 5.
    Hindo T, Chakrabartty S (2012) Noise-exploitation and adaptation in neuromorphic sensors. In: Proceeding of SPIE, bioinspiration, biomimetics, and bioreplication, vol 8339, March 2012Google Scholar
  6. 6.
    Chakrabartty S, Cauwenberghs G (2004) Margin normalization and propagation in analog vlsi. In: ISCAS (1)’04, pp 901–904Google Scholar
  7. 7.
    Loeliger HA (2004) An introduction to factor graphs. Signal Process Mag IEEE 21(1): 28–41.
  8. 8.
    Kong C, Chakrabartty S (2007) Analog iterative ldpc decoder based on margin propagation. Circuits Syst II Express Briefs IEEE Trans 54(12):1140–1144CrossRefGoogle Scholar
  9. 9.
    Gu M, Chakrabartty S (2012) Synthesis of bias-scalable cmos analog computational circuits using margin propagation. Circuits Syst I Regul Pap IEEE Trans 59(2):243–254MathSciNetCrossRefGoogle Scholar
  10. 10.
    Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Networks 14:1569–1572CrossRefGoogle Scholar
  11. 11.
    Gerstner W, Kistler WM (2002) Spiking neuron models: single neurons, populations, plasticity, 1st edn. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  12. 12.
    Segee B (1999) Methods in neuronal modeling: from ions to networks. Comput Sci Eng 1:81CrossRefGoogle Scholar
  13. 13.
    Indiveri G et al (2011) Neuromorphic silicon neuron circuits. Frontiers Neurosci 5:1–23Google Scholar
  14. 14.
    Sarpeshkar R (1998) Analog versus digital: extrapolating from electronics to neurobiology. Neural Comput 10(7):1601–1638CrossRefGoogle Scholar
  15. 15.
    Li Y, Shepard K, Tsividis Y (2005) Continuous-time digital signal processors. Asynchronous circuits and systems, ASYNC 2005. In: Proceedings of 11th IEEE international symposium, pp 138–143, March 2005Google Scholar
  16. 16.
    Hindo T (2012) Scalable pulsed computational module using integrate and fire structure and margin propagation algorithm. In: Proceedings of the world congress on engineering and computer science, vol 2. WCECS 2012, pp 860–865, Oct 2012Google Scholar
  17. 17.
    Gu M, Misra K, Radha H, Chakrabartty S (2009) Sparse decoding of low density parity check codes using margin propagation. In: Global telecommunications conference, vol 2009. GLOBECOM 2009. IEEE, pp 1–6, 30 Dec 2009Google Scholar
  18. 18.
    Hindo T (2012) An asynchronous, time-domain analog hidden markov circuit based on integrate and fire margin propagation. In: ASME: proceeding of 5th conference on computer and electrical engineering, Oct 2012Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingUSA

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