Abstract
Biological vision systems inspire processing methods in computer vision applications. This paper employs the insights of vision systems in hardware and presents a pixel-parallel, reconfigurable, and layer-based hierarchical architecture for smart image sensors. The architecture aims to bring computation close to the sensor to achieve high acceleration for different machine vision applications while consuming low power. We logically divide the image into multiple regions and perform pixel-level and region-level processing after removing spatiotemporal redundancy. Those processors use bio-inspired algorithms to activate the regions with region of interest of a scene. The hierarchical processing breaks the traditional sequential image processing and introduces parallelism for machine vision applications. Also, we make the hardware design reconfigurable even after fabrication to make the hardware reusable for different applications. Simulation results show that the area overhead and power penalty for adding reconfigurable features stay in an acceptable range. We emphasize to maximize the operating speed and obtain 800 MHz. Besides, the design saves 84.01% and 96.91% dynamic power at the first and second stages of the hierarchy by removing redundant information. Furthermore, the sequential deployment of high-level reasoning only on the selected regions of the image becomes computationally inexpensive to execute a complex task in real time.
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References
Kartheek Medathati, N.V., Neumann, H., Masson, G.S., Kornprobst, P.: Bio-inspired computer vision: towards a synergistic approach of artificial and biological vision. Comput. Vis. Image Underst. 150, 1–30 (2016)
Stroble, J.K., Stone, R.B., Watkins, S.E.: An overview of biomimetic sensor technology. Sens. Rev. 29(2), 112–119 (2009)
Koelling, T., Wang, J.: Advances in cmos image sensors. Secur. Deal. Integr. 30(10), 70–70,72,74, 10 (2008)
Zhu, H., Shibata, T.: A real-time motion-feature-extraction vlsi employing digital-pixel-sensor-based parallel architecture. IEEE Trans. Circuits Syst. Video Technol. 24(10), 1787–1799 (2014)
Tyrrell, B., Anderson, K., Baker, J., Berger, R., Brown, M., Colonero, C., Costa, J., Holford, B., Kelly, M., Ringdahl, E., Schultz, K., Wey, J.: Time delay integration and in-pixel spatiotemporal filtering using a nanoscale digital cmos focal plane readout. IEEE Trans. Electron Dev. 56(11), 2516–2523 (2009)
Gouveia, L.C.P., Choubey, B.: Advances on cmos image sensors. Sens. Rev. 36(3), 231–239 (2016)
Sakakibara, M., Ogawa, K., Sakai, S., Tochigi, Y., Honda, K., Kikuchi, H., Wada, T., Kamikubo, Y., Miura, T., Nakamizo, M., et al.: A 6.9-µm pixel-pitch back-illuminated global shutter cmos image sensor with pixel-parallel 14-bit subthreshold adc. IEEE J. Solid State Circuits 53(99), 1–9 (2018)
Delbrück, T., Linares-Barranco, B., Culurciello, E., Posch, C.: Activity-driven, event-based vision sensors. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp 2426–2429. IEEE (2010)
Atick, J.J., Norman Redlich, A.: Towards a theory of early visual processing. Neural Comput. 2(3), 308–320 (1990)
Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7(6), 1129–1159 (1995)
Foldiak, P.: Sparse coding in the primate cortex. The handbook of brain theory and neural networks (2003)
Huang, Y., Rao, R.P.N.: Predictive coding. Wiley Interdiscip. Rev. Cognit. Sci. 2(5), 580–593 (2011)
Bhowmik, P., Pantho, M.J.H., Asadinia, M., Bobda, C.: Design of a reconfigurable 3d pixel-parallel neuromorphic architecture for smart image sensor. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 673–681 (2018)
Nassi, J.J., Callaway, E.M.: Parallel processing strategies of the primate visual system. Nat. Rev. Neurosci. 10(5), 360 (2009)
Bhowmik, P., Pantho, M.J.H., Bobda, C.: Visual cortex inspired pixel-level re-configurable processors for smart image sensors. In: Proceedings of the 56th Annual Design Automation Conference 2019. ACM (2019)
Pantho, M.J.H., Bhowmik, P., Bobda, C.: Pixel-parallel architecture for neuromorphic smart image sensor with visual attention. In: 2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), pp. 245–250. IEEE (2018)
Vahid, F., Givargis, T.D.: Embedded System Design: A Unified Hardware/Software Introduction, Chapter 1. Wiley, New York (2002)
Van Essen, D.C., Anderson, C.H., Felleman, D.J.: Information processing in the primate visual system: an integrated systems perspective. Science 255(5043), 419–423 (1992)
Rodieck, R.W., Rodieck, R.W.: The First Steps in Seeing, vol. 1. Sinauer Associates Sunderland, Sunderland (1998)
Kastner, D.B., Baccus, S.A.: Insights from the retina into the diverse and general computations of adaptation, detection, and prediction. Curr. Opin. Neurobiol. 25, 63–69 (2014)
Rieke, F., Rudd, M.E.: The challenges natural images pose for visual adaptation. Neuron 64(5), 605–616 (2009)
Koch, G., Oliveri, M., Caltagirone, C.: Neural networks engaged in milliseconds and seconds time processing: evidence from transcranial magnetic stimulation and patients with cortical or subcortical dysfunction. Philos. Trans. R. Soc. B Biol. Sci. 364(1525), 1907–1918 (2009)
Larkum, M.: A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends Neurosci. 36(3), 141–151 (2013)
Motter, B.C.: Focal attention produces spatially selective processing in visual cortical areas v1, v2, and v4 in the presence of competing stimuli. J. Neurophysiol. 70(3), 909–919 (1993)
Michalareas, G., Vezoli, J., Van Pelt, S., Schoffelen, J.-M., Kennedy, H., Fries, P.: Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas. Neuron 89(2), 384–397 (2016)
Milner, P.M.: A model for visual shape recognition. Psychol. Rev. 81(6), 521 (1974)
Ahissar, M., Hochstein, S.: The reverse hierarchy theory of visual perceptual learning. Trends Cognit. Sci. 8(10), 457–464 (2004)
Gur, M.: Space reconstruction by primary visual cortex activity: a parallel, non-computational mechanism of object representation. Trends Neurosci. 38(4), 207–216 (2015)
Hochstein, S., Ahissar, M.: View from the top: hierarchies and reverse hierarchies in the visual system. Neuron 36(5), 791–804 (2002)
Lee, T.S., Mumford, D.: Hierarchical bayesian inference in the visual cortex. JOSA A 20(7), 1434–1448 (2003)
Reynolds, J.H., Pasternak, T., Desimone, R.: Attention increases sensitivity of v4 neurons. Neuron 26(3), 703–714 (2000)
Bylinskii, Z., DeGennaro, E.M., Rajalingham, R., Ruda, H., Zhang, J., Tsotsos, J.K.: Towards the quantitative evaluation of visual attention models. Vis. Res. 116, 258–268 (2015)
Itti, L., Koch, C.: Computational modelling of visual attention. Nat. Rev. Neurosci. 2(3), 194 (2001)
Tsotsos, J.K., Eckstein, M.P., Landy, M.S.: Computational models of visual attention. Vis. Res. 116(Pt B), 93 (2015)
Leon-Salas, W.D., Balkir, S., Sayood, K., Schemm, N., Hoffman, M.W.: A cmos imager with focal plane compression using predictive coding. IEEE J. Solid State Circuits 42(11), 2555–2572 (2007)
Miao, W., Lin, Q., Nanjian, W.: A novel vision chip for high-speed target tracking. Jpn. J. Appl. Phys. 46(4S), 2220 (2007)
Moini, A., Bouzerdoum, A., Eshraghian, K., Yakovleff, A., Nguyen, X.T., Blanksby, A., Beare, R., Abbott, D., Bogner, R.E.: An insect vision-based motion detection chip. IEEE J. Solid State Circuits 32(2), 279–284 (1997)
Oike, Y., Ikeda, M., Asada, K.: A 375/spl times/365 high-speed 3-d range-finding image sensor using row-parallel search architecture and multisampling technique. IEEE J Solid State Circuits 40(2), 444–453 (2005)
Zhang, W., Qiuyu, F., Nan-Jian, W.: A programmable vision chip based on multiple levels of parallel processors. IEEE J. Solid State Circuits 46(9), 2132–2147 (2011)
Culurciello, E., Etienne-Cummings, R., Boahen, K.A.: A biomorphic digital image sensor. IEEE J. Solid State Circuits 38(2), 281–294 (2003)
Shoushun, C., Bermak, A.: Arbitrated time-to-first spike cmos image sensor with on-chip histogram equalization. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 15(3), 346–357 (2007)
Serrano-Gotarredona, T., Linares-Barranco, B.: A 128x128 1.5% contrast sensitivity 0.9% fpn 3 us latency 4 mw asynchronous frame-free dynamic vision sensor using transimpedance preamplifiers. IEEE J. Solid State Circuits 48(3), 827–838 (2013)
Delbruck, T.: Frame-free dynamic digital vision. In: Proceedings of International Symposium on Secure-Life Electronics, Advanced Electronics for Quality Life and Society, pp. 21–26 (2008)
Possa, P., Harb, N., Dokladalova, E., Valderrama, C.: P2ip: a novel low-latency programmable pipeline image processor. Microprocess. Microsyst. 39(7), 529–540 (2015)
Serrano-Gotarredona, R., Serrano-Gotarredona, T., Acosta-Jiménez, A., Serrano-Gotarredona, C., Pérez-Carrasco, J.A., Linares-Barranco, B., Linares-Barranco, A., Jiménez-Moreno, G., Civit-Ballcels, A.: On real-time aer 2-d convolutions hardware for neuromorphic spike-based cortical processing. IEEE Trans. Neural Netw. 19(7), 1196–1219 (2008)
Camunas-Mesa, L., Acosta-Jimenez, A., Zamarreño-Ramos, C., Serrano-Gotarredona, T., Linares-Barranco, B.: A 32x32 pixel convolution processor chip for address event vision sensors with 155 ns event latency and 20 meps throughput. IEEE Trans. Circuits Syst. I Regul. Pap. 58(4), 777–790 (2010)
Rao, R.P.N., Ballard, D.H.: Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2(1), 79 (1999)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Acknowledgements
We acknowledge Sakakibara et al. [7] for the excellent work of pixel parallel image sensor. We are thankful to the National Science Foundation (NSF) for supporting us by Grant-1618606.
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Bhowmik, P., Pantho, M.J.H. & Bobda, C. Bio-inspired smart vision sensor: toward a reconfigurable hardware modeling of the hierarchical processing in the brain. J Real-Time Image Proc 18, 157–174 (2021). https://doi.org/10.1007/s11554-020-00960-5
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DOI: https://doi.org/10.1007/s11554-020-00960-5