A Generalized Stochastic Implementation of the Disparity Energy Model for Depth Perception
- 206 Downloads
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.
KeywordsStochastic computing Neuromorphic computing Approximate computing Gabor filters Disparity-energy model Computer vision Biomedical electronics Neural networks
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.
- 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.Google Scholar
- 2.Alaghi, A., Li, C., & Hayes, J. P. (2013). Stochastic circuits for real-time image-processing applications. In Design Automation Conference.Google Scholar
- 3.Alfke, P. (1998). Efficient shift registers, LFSR counters, and long pseudo-random sequence generators. http://www.xilinx.com/bvdocs/appnotes/xapp052.pdf.
- 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).Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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.Google Scholar
- 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).Google Scholar
- 16.Ma, C., Zhong, S., & Dang, H. (2012). High fault tolerant image processing system based on stochastic computing.Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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).Google Scholar