A Generalized Stochastic Implementation of the Disparity Energy Model for Depth Perception

Abstract

Implementing neuromorphic algorithms is increasingly interesting as the error resilience and low-area, low-energy nature of biological systems becomes the potential solution for problems in robotics and artificial intelligence. While conventional digital methods are inefficient in implementing massively parallel systems, analog solutions are hard to design and program. Stochastic Computing (SC) is a natural bridge that allows pseudo-analog computations in the digital domain using low complexity hardware. However, large scale SC systems traditionally suffered from long latencies, hence higher energy consumption. This work develops a VLSI architecture for an SC based binocular vision system based on a disparity-energy model that emulates the hierarchical multi-layered neural structure in the primary visual cortex. The 3-layer neural network architecture is biologically plausible and is tuned to detecting 5 different disparities. The architecture is compact, adder-free, and achieves better disparity detection compared to a floating-point version by using a modified disparity-energy model. A generalized 1x100 pixel processing system is synthesized using TSMC 65nm CMOS technology and it achieves 71 % reduction in area-delay product and 48 % in energy savings compared to a fixed-point implementation at equivalent precision.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27

References

  1. 1.

    Alaghi, A., & Hayes, J. P. (2013). Exploiting correlation in stochastic circuit design. In IEEE 31st International Conference on Computer Design (ICCD) (pp. 39–46): IEEE.

  2. 2.

    Alaghi, A., Li, C., & Hayes, J. P. (2013). Stochastic circuits for real-time image-processing applications. In Design Automation Conference.

  3. 3.

    Alfke, P. (1998). Efficient shift registers, LFSR counters, and long pseudo-random sequence generators. http://www.xilinx.com/bvdocs/appnotes/xapp052.pdf.

  4. 4.

    Anzai, A., Ohzawa, I., & Freeman, R. D. (1997). Neural mechanisms underlying binocular fusion and stereopsis: position vs. phase. Proceedings of the National Academy of Sciences, 94(10), 5438–5443.

    Article  Google Scholar 

  5. 5.

    Boga, K., Onizawa, N., Leduc-Primeau, F., Matsumiya, K., Hanyu, T., & Gross, W. J. (2015). Stochastic implementation of the disparity energy model for depth perception. In IEEE Workshop on Signal Processing Systems (SiPS) (pp. 1–6).

  6. 6.

    Brown, B. D., & Card, H. C. (2001). Stochastic neural computation. I. computational elements. IEEE Computer, 50(9), 891–905.

    MathSciNet  Article  Google Scholar 

  7. 7.

    Chang, Y. -N., & Parhi, K. K. (2013). Architectures for digital filters using stochastic computing. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2697–2701).

  8. 8.

    Chen, J., & Hu, J. (2013). A novel FIR filter based on stochastic logic. In Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 2050–2053).

  9. 9.

    Choi, T. Y. W., Merolla, P. A., Arthur, J. V., Boahen, K. A., & Shi, B. E. (2005). Neuromorphic implementation of orientation hypercolumns. IEEE Transactions Circuits Systems I, Reg Papers, 52(6), 1049–1060.

    MathSciNet  Article  MATH  Google Scholar 

  10. 10.

    Cumming, B. G., & Parker, A. J. (1997). Responses of primary visual cortical neurons to binocular disparity without depth perception. Nature, 389(6648), 280–283.

    Article  Google Scholar 

  11. 11.

    Janer, C. L., Quero, J. M., Ortega, J. G., & Franquelo, L. G. (1996). Fully parallel stochastic computation architecture. IEEE Signal Processing, 44(8), 2110–2117.

    Article  Google Scholar 

  12. 12.

    Leduc-Primeau, F., Gaudet, V. C., & Gross, W. J. (2015). Stochastic decoders for LDPC codes. In Advanced Hardware Design for Error Correcting Codes (pp. 105–128): Springer.

  13. 13.

    Li, P., Lilja, D. J., Qian, W., Bazargan, K., & Riedel, M. D. (2014). Computation on stochastic bit streams digital image processing case studies. IEEE Trans Very Large Scale Integration (VLSI) System, 22(3), 449–462.

    Article  Google Scholar 

  14. 14.

    Li, Y., & Hu, J. (2013). A novel implementation scheme for high area-efficient dct based on signed stochastic computation. In Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 990?-993).

  15. 15.

    Liu, S. -C., Kramer, J., Indiveri, G., Delbruèck, T., Burg, T.s, & Douglas, R. (2001). Orientation-selective aVLSI spiking neurons. Neural Networks, 14(6), 629–643.

    Article  Google Scholar 

  16. 16.

    Ma, C., Zhong, S., & Dang, H. (2012). High fault tolerant image processing system based on stochastic computing.

  17. 17.

    McMahon, M. J., Packer, O. S., & Dacey, D. M. (2004). The classical receptive field surround of primate parasol ganglion cells is mediated primarily by a non-gabaergic pathway. The Journal of neuroscience, 24(15), 3736–3745.

    Article  Google Scholar 

  18. 18.

    Mutch, J., & Lowe, D. G. (2008). Object class recognition and localization using sparse features with limited receptive fields. International Journal of Computer Vision, 80(1), 45–57.

    Article  Google Scholar 

  19. 19.

    Ohzawa, I., Deangelis, G. C., & Freeman, R. D. (1990). Stereoscopic depth discrimination in the visual cortex: neurons ideally suited as disparity detectors. Science, 249(4972), 1037–1041.

    Article  Google Scholar 

  20. 20.

    Onizawa, N., Katagiri, D., Gross, W. J., & Hanyu, T. Analog-to-stochastic converter using magnetic tunnel junction devices for vision chips. In IEEE Transactions on Nanotechnology, 2016(to appear).

  21. 21.

    Onizawa, N., Katagiri, D., Matsumiya, K., Gross, W. J., & Hanyu, T. (2015). Gabor filter based on stochastic computation. IEEE Signal Process Letter, 22(9), 1224–1228.

    Article  Google Scholar 

  22. 22.

    Qian, N. (1997). Binocular disparity and the perception of depth. Neuron, 18(3), 359–368.

    Article  Google Scholar 

  23. 23.

    Shimonomura, K., Kushima, T., & Yagi, T. (2008). Binocular robot vision emulating disparity computation in the primary visual cortex. Neural Networks, 21(2), 331–340.

    Article  Google Scholar 

  24. 24.

    Tehrani, S. S., Naderi, A., Kamendje, G. -A., Hemati, S., Mannor, S., & Gross, W. J. (2010). Majority-based tracking forecast memories for stochastic LDPC decoding. IEEE Transactions Signal Processing, 58 (9).

  25. 25.

    Wang, R., Han, J., Cockburn, B., & Elliott, D. (2015). Design and evaluation of stochastic FIR filters. In IEEE Pacific Rim Conf. on Communications, Computers and Signal Processing (PACRIM) (pp. 407–412).

Download references

Acknowledgments

The authors would like to thank Hasan Mozafari, Arash Ardakani and Xinchi Chen for useful discussions. Warren J. Gross is a member of ReSMiQ (Regroupement Stratégique en Microsystémes du Québec) and SYTACom (Centre de recherche sur les systèmes et les technologies avancés en communications). This work was supported by the Brainware LSI Project of MEXT (Ministry of education, culture, sports, science and technology), Japan.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Kaushik Boga.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Boga, K., Leduc-Primeau, F., Onizawa, N. et al. A Generalized Stochastic Implementation of the Disparity Energy Model for Depth Perception. J Sign Process Syst 90, 709–725 (2018). https://doi.org/10.1007/s11265-016-1197-3

Download citation

Keywords

  • Stochastic computing
  • Neuromorphic computing
  • Approximate computing
  • Gabor filters
  • Disparity-energy model
  • Computer vision
  • Biomedical electronics
  • Neural networks