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Cain: Automatic Code Generation for Simultaneous Convolutional Kernels on Focal-plane Sensor-processors

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Languages and Compilers for Parallel Computing (LCPC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13149))

Abstract

Focal-plane Sensor-processors (FPSPs) are a camera technology that enable low power, high frame rate computation, making them suitable for edge computation. Unfortunately, these devices’ limited instruction sets and registers make developing complex algorithms difficult. In this work, we present Cain, an open-source compiler that targets SCAMP-5, a general-purpose FPSP – which generates code from multiple convolutional kernels. As an example, given the convolutional kernels for an MNIST digit recognition neural network, Cain produces code that is half as long, when compared to the other available compilers for SCAMP-5.

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Notes

  1. 1.

    Single-entry matrix. Not to be confused with identity matrix.

  2. 2.

    github.com/najiji/auto_code_cpa/tree/75c017e5ad28c0f3f040fb9f84d7f8727d035baa.

References

  1. Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation OSDI 16, pp. 265–283 (2016)

    Google Scholar 

  2. Barthels, H., Psarras, C., Bientinesi, P.: Linnea: Automatic generation of efficient linear algebra programs (2019). https://arxiv.org/pdf/1912.12924.pdf

  3. Bose, L., Chen, J., Carey, S.J., Dudek, P., Mayol-Cuevas, W.: Visual odometry for pixel processor arrays. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 4614–4622 (October 2017)

    Google Scholar 

  4. Bose, L., Chen, J., Carey, S.J., Dudek, P., Mayol-Cuevas, W.: A camera that CNNs: towards embedded neural networks on pixel processor arrays. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1335–1344 (2019)

    Google Scholar 

  5. Carey, S.J., Barr, D.R.W., Wang, B., Lopich, A., Dudek, P.: Locating high speed multiple objects using a SCAMP-5 vision-chip. In: 2012 13th International Workshop on Cellular Nanoscale Networks and their Applications, pp. 1–2 (August 2012)

    Google Scholar 

  6. Carey, S.J., Lopich, A., Barr, D.R.W., Wang, B., Dudek, P.: A 100,000 fps vision sensor with embedded 535GOPS/W \(256\times 256\) SIMD processor array. In: 2013 Symposium on VLSI Circuits, pp. C182–C183 (2013)

    Google Scholar 

  7. Chandra, A., Chattopadhyay, S.: Design of hardware efficient FIR filter: a review of the state-of-the-art approaches. Eng. Sci. Technol. Int. J. 19(1), 212–226 (2016)

    Google Scholar 

  8. Chen, J.: scamp5 kernel api macro analog.hpp file reference (January 2020). https://scamp.gitlab.io/scamp5d_doc/scamp5__kernel__api__macro__analog_8hpp.html

  9. Chen, Y.H., Krishna, T., Emer, J.S., Sze, V.: Eyeriss: an energy-efficient reconfigurable accelerator for deep convolutional neural networks. IEEE J. Solid-State Circuits 52(1), 127–138 (2016)

    Article  Google Scholar 

  10. Debrunner, T., Saeedi, S., Bose, L., Davison, A.J., Kelly, P.H.J.: Camera tracking on focal-plane sensor-processor arrays (2019)

    Google Scholar 

  11. Debrunner, T., Saeedi, S., Kelly, P.H.J.: AUKE: automatic kernel code generation for an analogue SIMD focal-plane sensor-processor array. ACM Trans. Archit. Code Optim. 15(4), 59:1–59:26 (2019)

    Google Scholar 

  12. Guillard, B.: CNNs-on-FPSPs (May 2019). https://github.com/brouwa/CNNs-on-FPSPs/tree/c6b5c51839e9e3c453681e5b0a3e3ef541ba3cce

  13. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  14. Martel, J.: Unconventional Processing with Unconventional Visual Sensing. Ph.D. thesis, Institut National des Sciences Appliquées de Lyon (2019)

    Google Scholar 

  15. Martel, J.N.P., Müller, L.K., Carey, S.J., Dudek, P., Wetzstein, G.: Neural sensors: learning pixel exposures for HDR imaging and video compressive sensing with programmable sensors. IEEE Trans. Pattern Anal. Mach. Intell. 42(7), 1642–1653 (2020)

    Article  Google Scholar 

  16. Murai, R., Saeedi, S., Kelly, P.H.J.: BIT-VO: visual odometry at 300 FPS using Binary features from the focal plane. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020)

    Google Scholar 

  17. Saeedi, S., Bodin, B., Wagstaff, H., et al.: Navigating the landscape for real-time localization and mapping for robotics and virtual and augmented reality. Proc. IEEE 106(11), 2020–2039 (2018)

    Article  Google Scholar 

  18. Stow, E.: Automatic Code Generation for Simultaneous Convolutional Kernels on Cellular Processor Arrays. Master’s thesis, Imperial College London (2020). http://edstow.co.uk/pub/2020/MEngThesis.pdf

  19. Stow, E., Murai, R.: ed741/cain: 3.0-experiments.1 (August 2020). https://doi.org/10.5281/zenodo.3975615

  20. Wong, M.Z., Guillard, B., Murai, R., Saeedi, S., Kelly, P.H.J.: AnalogNet: Convolutional Neural Network Inference on Analog Focal Plane Sensor Processors. arXiv preprint arXiv:2006.01765 (2020)

  21. XIMEA: xiB - PCI Express Cameras with high speed and resolution. https://www.ximea.com/pci-express-camera/pci-express-camera

  22. Zarándy, Á.: Focal-Plane Sensor-Processor Chips. SpringerLink : Bücher, Springer, New York (2011). https://doi.org/10.1007/978-1-4419-6475-5

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Acknowledgements

We would like to thank Piotr Dudek, Stephen J. Carey, and Jianing Chen at the University of Manchester for kindly providing access to SCAMP-5, and their support in our work. This work was partially supported by the EPSRC, grant reference EP/P010040/1.

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Stow, E., Murai, R., Saeedi, S., Kelly, P.H.J. (2022). Cain: Automatic Code Generation for Simultaneous Convolutional Kernels on Focal-plane Sensor-processors. In: Chapman, B., Moreira, J. (eds) Languages and Compilers for Parallel Computing. LCPC 2020. Lecture Notes in Computer Science(), vol 13149. Springer, Cham. https://doi.org/10.1007/978-3-030-95953-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-95953-1_13

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