Abstract

This paper presents an all optical fiber based implementation of a hybrid analog-digital computational primitive that provides a basis for complex processing on high bandwidth signals. A natural implementation of a hybrid analog/digital photonic processing primitive is achieved through the integration of new nonlinear fiber, and exploitation of the physics of semiconductor device to process signals in unique ways. Specifically, we describe the use of a semiconductor optical amplifier to implement leaky temporal integration of a signal and a highly Ge-doped nonlinear fiber for thresholding. A straightforward correspondence between our computational primitive and leaky-integrate-and-fire neurons permits leveraging of a large body of research characterizing the computational capabilities of these devices and the emerging pulse processing computational paradigm as a means to implement practical signal processing algorithms in hybrid computing platforms. An experimental demonstration of the behavior of the pulse processing primitive is presented.

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References

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  1. C. Koch, Biophysics of Computation (Oxford University Press, 1999).
  2. Y. Abu-Mostafa and D. Psaltis, “Optical neural computers (invited),” Sci. Am. 256, 88–95 (1987).
    [CrossRef]
  3. S. Jutamulia and F. T. S. Yu, “Overview of hybrid optical neural networks,” Opt. Laser Technol. 28(2), 59–72 (1996).
    [CrossRef]
  4. M. T. Hill, E. E. E. Frietman, H. de Waardt, G. D. Khoe, and H. S. Dorren, “All fiber-optic neural network using coupled SOA based ring lasers,” IEEE Trans. Neural Netw. 13(6), 1504–1513 (2002).
    [CrossRef] [PubMed]
  5. R. Sarpeshkar, “Analog versus digital: extrapolating from electronics to neurobiology,” Neural Comput. 10(7), 1601–1638 (1998).
    [CrossRef] [PubMed]
  6. W. Maass, and C. M. Bishop, eds., Pulsed Neural Networks (The MIT Press, 1999).
  7. M. Premaratne, D. Neˇsi’c, and G. P. Agrawal, “Pulse amplification and gain recovery in semiconductor optical amplifiers: A systematic analytical approach,” J. Lightwave Technol. 26(12), 1653–1660 (2008).
    [CrossRef]
  8. K. Kravtsov, P. R. Prucnal, and M. M. Bubnov, “Simple nonlinear interferometer-based all-optical thresholder and its applications for optical CDMA,” Opt. Express 15(20), 13114–13122 (2007).
    [CrossRef] [PubMed]
  9. E. M. Dianov and V. M. Mashinsky, “Germania-based core optical fibers,” J. Lightwave Technol. 23(11), 3500–3508 (2005).
    [CrossRef]

2008 (1)

2007 (1)

2005 (1)

2002 (1)

M. T. Hill, E. E. E. Frietman, H. de Waardt, G. D. Khoe, and H. S. Dorren, “All fiber-optic neural network using coupled SOA based ring lasers,” IEEE Trans. Neural Netw. 13(6), 1504–1513 (2002).
[CrossRef] [PubMed]

1998 (1)

R. Sarpeshkar, “Analog versus digital: extrapolating from electronics to neurobiology,” Neural Comput. 10(7), 1601–1638 (1998).
[CrossRef] [PubMed]

1996 (1)

S. Jutamulia and F. T. S. Yu, “Overview of hybrid optical neural networks,” Opt. Laser Technol. 28(2), 59–72 (1996).
[CrossRef]

1987 (1)

Y. Abu-Mostafa and D. Psaltis, “Optical neural computers (invited),” Sci. Am. 256, 88–95 (1987).
[CrossRef]

Abu-Mostafa, Y.

Y. Abu-Mostafa and D. Psaltis, “Optical neural computers (invited),” Sci. Am. 256, 88–95 (1987).
[CrossRef]

Agrawal, G. P.

Bubnov, M. M.

de Waardt, H.

M. T. Hill, E. E. E. Frietman, H. de Waardt, G. D. Khoe, and H. S. Dorren, “All fiber-optic neural network using coupled SOA based ring lasers,” IEEE Trans. Neural Netw. 13(6), 1504–1513 (2002).
[CrossRef] [PubMed]

Dianov, E. M.

Dorren, H. S.

M. T. Hill, E. E. E. Frietman, H. de Waardt, G. D. Khoe, and H. S. Dorren, “All fiber-optic neural network using coupled SOA based ring lasers,” IEEE Trans. Neural Netw. 13(6), 1504–1513 (2002).
[CrossRef] [PubMed]

Frietman, E. E. E.

M. T. Hill, E. E. E. Frietman, H. de Waardt, G. D. Khoe, and H. S. Dorren, “All fiber-optic neural network using coupled SOA based ring lasers,” IEEE Trans. Neural Netw. 13(6), 1504–1513 (2002).
[CrossRef] [PubMed]

Hill, M. T.

M. T. Hill, E. E. E. Frietman, H. de Waardt, G. D. Khoe, and H. S. Dorren, “All fiber-optic neural network using coupled SOA based ring lasers,” IEEE Trans. Neural Netw. 13(6), 1504–1513 (2002).
[CrossRef] [PubMed]

Jutamulia, S.

S. Jutamulia and F. T. S. Yu, “Overview of hybrid optical neural networks,” Opt. Laser Technol. 28(2), 59–72 (1996).
[CrossRef]

Khoe, G. D.

M. T. Hill, E. E. E. Frietman, H. de Waardt, G. D. Khoe, and H. S. Dorren, “All fiber-optic neural network using coupled SOA based ring lasers,” IEEE Trans. Neural Netw. 13(6), 1504–1513 (2002).
[CrossRef] [PubMed]

Kravtsov, K.

Mashinsky, V. M.

Ne?si’c, D.

Premaratne, M.

Prucnal, P. R.

Psaltis, D.

Y. Abu-Mostafa and D. Psaltis, “Optical neural computers (invited),” Sci. Am. 256, 88–95 (1987).
[CrossRef]

Sarpeshkar, R.

R. Sarpeshkar, “Analog versus digital: extrapolating from electronics to neurobiology,” Neural Comput. 10(7), 1601–1638 (1998).
[CrossRef] [PubMed]

Yu, F. T. S.

S. Jutamulia and F. T. S. Yu, “Overview of hybrid optical neural networks,” Opt. Laser Technol. 28(2), 59–72 (1996).
[CrossRef]

IEEE Trans. Neural Netw. (1)

M. T. Hill, E. E. E. Frietman, H. de Waardt, G. D. Khoe, and H. S. Dorren, “All fiber-optic neural network using coupled SOA based ring lasers,” IEEE Trans. Neural Netw. 13(6), 1504–1513 (2002).
[CrossRef] [PubMed]

J. Lightwave Technol. (2)

Neural Comput. (1)

R. Sarpeshkar, “Analog versus digital: extrapolating from electronics to neurobiology,” Neural Comput. 10(7), 1601–1638 (1998).
[CrossRef] [PubMed]

Opt. Express (1)

Opt. Laser Technol. (1)

S. Jutamulia and F. T. S. Yu, “Overview of hybrid optical neural networks,” Opt. Laser Technol. 28(2), 59–72 (1996).
[CrossRef]

Sci. Am. (1)

Y. Abu-Mostafa and D. Psaltis, “Optical neural computers (invited),” Sci. Am. 256, 88–95 (1987).
[CrossRef]

Other (2)

C. Koch, Biophysics of Computation (Oxford University Press, 1999).

W. Maass, and C. M. Bishop, eds., Pulsed Neural Networks (The MIT Press, 1999).

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Figures (4)

Fig. 1
Fig. 1

Block diagram of the photonic neuron. G: gain; T: time delay; SOA semiconductor optical amplifier: HD nonlinear fiber: highly Ge-doped nonlinear fiber. The three processing are (i) passive weighting, delay, and summation of inputs; (ii) temporal integration; and (iii) thresholding.

Fig. 2
Fig. 2

Experimental setup. G: variable attenuator, T: variable delay line, PC: polarization controller, TI: tunable isolator [8], EDFA: erbium-doped fiber amplifier.

Fig. 3
Fig. 3

The measured SOA response to excitation by multiple pulses. (a) Sampled SOA gain dynamics; (b) Oscilloscope trace of the control pulse sequence. The SOA recovery time (integration time constant) is 180 ps, resting potential level equals 43 fJ.

Fig. 4
Fig. 4

An experimental demonstration of the all-optical thresholder operation. A — the measured input signal, which is a sum of two pulse streams; B — the integrator output; C — the thesholded signal, measured at two different threshold positions.

Equations (1)

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Activation Active   pumping Leakage External   input d V m ( t ) d t = V rest τ m V m ( t ) τ m 1 C m V m ( t ) σ ( t ) ( 1 ) d N ( t ) d t = N rest τ e N ( t ) τ e Γ α E p N ( t ) I ( t ) ( 2 )

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