A vision chip with complementary pathways for open-world sensing

  • Fossum, E. R. CMOS image sensors: Electronic camera-on-a-chip. IEEE Trans. Electron Devices 44, 1689–1698 (1997).

    Article 
    ADS 

    Google Scholar
     

  • Gove, R. J. in High Performance Silicon Imaging 2nd edn (ed. Durini, D.) 185–240 (Elsevier, 2019).

  • Yun, S. H. & Kwok, S. J. Light in diagnosis, therapy and surgery. Nat. Biomed. Eng. 1, 0008 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, Z., Ukida, H., Ramuhalli, P. & Niel, K (eds). Integrated Imaging and Vision Techniques for Industrial Inspection (Springer, 2015).

  • Nakamura, J. Image Sensors and Signal Processing for Digital Still Cameras (CRC Press, 2017).

  • Bogdoll, D., Nitsche, M. & Zöllner, M. Anomaly detection in autonomous driving: a survey. In Proc. IEEE/CVF International Conference on Computer Vision and Pattern Recognition 4488–4499 (CVF, 2022).

  • Hanheide, M. et al. Robot task planning and explanation in open and uncertain worlds. Artif. Intell. 247, 119–150 (2017).

    Article 
    MathSciNet 

    Google Scholar
     

  • Sarker, I. H. Machine learning: algorithms, real-world applications and research directions. SN Comp. Sci. 2, 160 (2021).

    Article 

    Google Scholar
     

  • Joseph, K., Khan, S., Khan, F. S. & Balasubramanian, V. N. Towards open world object detection. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 5830–5840 (CVF, 2021).

  • Breitenstein, J., Termöhlen, J.-A., Lipinski, D. & Fingscheidt, T. Breitenstein, J., Termöhlen, J.-A., Lipinski, D. & Fingscheidt, T. Systematization of corner cases for visual perception in automated driving. In 2020 IEEE Intelligent Vehicles Symposium (IV) 1257–1264 (IEEE, 2020).

  • Yan, C., Xu, W. & Liu, J. Can you trust autonomous vehicles: contactless attacks against sensors of self-driving vehicle. In Proc. Def Con 24, 109 (ACM, 2016).

  • Li, M., Wang, Y.-X. & Ramanan, D. Towards streaming perception. In European Conf. Computer Vision 473–488 (Springer, 2020).

  • Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. & Lawrence, N. D. Dataset Shift in Machine Learning (Mit Press, 2008).

  • Khatab, E., Onsy, A., Varley, M. & Abouelfarag, A. Vulnerable objects detection for autonomous driving: a review. Integration 78, 36–48 (2021).

    Article 

    Google Scholar
     

  • Shu, X. & Wu, X. Real-time high-fidelity compression for extremely high frame rate video cameras. IEEE Trans. Comput. Imaging 4, 172–180 (2017).

    Article 
    MathSciNet 

    Google Scholar
     

  • Feng, S. et al. Dense reinforcement learning for safety validation of autonomous vehicles. Nature 615, 620–627 (2023).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Goodale, M. A. & Milner, A. D. Separate visual pathways for perception and action. Trends Neurosci. 15, 20–25 (1992).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Nassi, J. J. & Callaway, E. M. Parallel processing strategies of the primate visual system. Nat. Rev. Neurosci. 10, 360–372 (2009).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mahowald, M. & Mahowald, M. in An Analog VLSI System for Stereoscopic Vision (ed. Mahowald, M.) 4–65 (Kluwer, 1994).

  • Zaghloul, K. A. & Boahen, K. Optic nerve signals in a neuromorphic chip I: Outer and inner retina models. IEEE Trans. Biomed. Eng. 51, 657–666 (2004).

    Article 
    PubMed 

    Google Scholar
     

  • Son, B. et al. 4.1 A 640 × 480 dynamic vision sensor with a 9 µm pixel and 300 Meps address-event representation. In 2017 IEEE International Solid-State Circuits Conference (ISSCC) 66–67 (IEEE, 2017).

  • Kubendran, R., Paul, A. & Cauwenberghs, G. A 256 × 256 6.3 pJ/pixel-event query-driven dynamic vision sensor with energy-conserving row-parallel event scanning. In 2021 IEEE Custom Integrated Circuits Conference (CICC) 1–2 (IEEE, 2021).

  • Posch, C., Matolin, D. & Wohlgenannt, R. A QVGA 143 dB dynamic range frame-free PWM image sensor with lossless pixel-level video compression and time-domain CDS. IEEE J. Solid-State Circuits 46, 259–275 (2010).

    Article 
    ADS 

    Google Scholar
     

  • Leñero-Bardallo, J. A., Serrano-Gotarredona, T. & Linares-Barranco, B. A 3.6 μs latency asynchronous frame-free event-driven dynamic-vision-sensor. IEEE J Solid-State Circuits 46, 1443–1455 (2011).

    Article 
    ADS 

    Google Scholar
     

  • Prophesee. IMX636ES (HD) https://www.prophesee.ai/event-camera-evk4/ (2021).

  • Brandli, C., Berner, R., Yang, M., Liu, S.-C. & Delbruck, T. A 240 × 180 130 db 3 µs latency global shutter spatiotemporal vision sensor. IEEE J. Solid-State Circuits 49, 2333–2341 (2014).

    Article 
    ADS 

    Google Scholar
     

  • Guo, M. et al. A 3-wafer-stacked hybrid 15MPixel CIS + 1 MPixel EVS with 4.6GEvent/s readout, in-pixel TDC and on-chip ISP and ESP function. In 2023 IEEE International Solid-State Circuits Conference (ISSCC) 90–92 (IEEE, 2023).

  • Kodama, K. et al. 1.22 μm 35.6Mpixel RGB hybrid event-based vision sensor with 4.88 μm-pitch event pixels and up to 10 K event frame rate by adaptive control on event sparsity. In 2023 IEEE International Solid-State Circuits Conference (ISSCC) 92–94 (IEEE, 2023).

  • Frohmader, K. P. A novel MOS compatible light intensity-to-frequency converter suited for monolithic integration. IEEE J. Solid-State Circuits 17, 588–591 (1982).

    Article 
    ADS 

    Google Scholar
     

  • Huang, T. et al. 1000× faster camera and machine vision with ordinary devices. Engineering 25, 110–119 (2023).

    Article 

    Google Scholar
     

  • Wang, X., Wong, W. & Hornsey, R. A high dynamic range CMOS image sensor with inpixel light-to-frequency conversion. IEEE Trans. Electron Devices 53, 2988–2992 (2006).

    Article 
    ADS 

    Google Scholar
     

  • Ng, D. C. et al. Pulse frequency modulation based CMOS image sensor for subretinal stimulation. IEEE Trans. Circuits Syst. II Express Briefs 53, 487–491 (2006).

    Article 

    Google Scholar
     

  • Culurciello, E., Etienne-Cummings, R. & Boahen, K. A. A biomorphic digital image sensor. IEEE J. Solid-State Circuits 38, 281–294 (2003).

    Article 
    ADS 

    Google Scholar
     

  • 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, 346–357 (2007).

    Article 

    Google Scholar
     

  • Guo, X., Qi, X. & Harris, J. G. A time-to-first-spike CMOS image sensor. IEEE Sens. J. 7, 1165–1175 (2007).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Shi, C. et al. A 1000 fps vision chip based on a dynamically reconfigurable hybrid architecture comprising a PE array processor and self-organizing map neural network. IEEE J. Solid-State Circuits 49, 2067–2082 (2014).

    Article 
    ADS 

    Google Scholar
     

  • Hsu, T.-H. et al. A 0.8 V intelligent vision sensor with tiny convolutional neural network and programmable weights using mixed-mode processing-in-sensor technique for image classification. In 2022 IEEE International Solid-State Circuits Conference (ISSCC) 1–3 (IEEE, 2022).

  • Lefebvre, M., Moreau, L., Dekimpe, R. & Bol, D. 7.7 A 0.2-to-3.6TOPS/W programmable convolutional imager SoC with in-sensor current-domain ternary-weighted MAC operations for feature extraction and region-of-interest detection. In 2021 IEEE International Solid-State Circuits Conference (ISSCC) 118–120 (IEEE, 2021).

  • Ishikawa, M., Ogawa, K., Komuro, T. & Ishii, I. A CMOS vision chip with SIMD processing element array for 1 ms image processing. In 1999 IEEE International Solid-State Circuits Conference 206–207 (IEEE, 1999).

  • Shi, Y.-Q. & Sun, H. Image and Video Compression for Multimedia Engineering: Fundamentals, Algorithms, and Standards 3rd edn (CRC Press, 2019).

  • Sakakibara, 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, 3017–3025 (2018).

    Article 
    ADS 

    Google Scholar
     

  • Seo, M.-W. et al. 2.45 e-rms low-random-noise, 598.5 mW low-power, and 1.2 kfps high-speed 2-Mp global shutter CMOS image sensor with pixel-level ADC and memory. IEEE J. Solid-State Circuits 57, 1125–1137 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Bogaerts, J. et al. 6.3 105 × 65 mm2 391Mpixel CMOS image sensor with >78 dB dynamic range for airborne mapping applications. In 2016 IEEE International Solid-State Circuits Conference (ISSCC) 114–115 (IEEE, 2016).

  • Park, I., Park, C., Cheon, J. & Chae, Y. 5.4 A 76 mW 500 fps VGA CMOS image sensor with time-stretched single-slope ADCs achieving 1.95e random noise. In 2019 IEEE International Solid-State Circuits Conference (ISSCC) 100–102 (IEEE, 2019).

  • Oike, Y. et al. 8.3 M-pixel 480-fps global-shutter CMOS image sensor with gain-adaptive column ADCs and chip-on-chip stacked integration. IEEE J. Solid-State Circuits 52, 985–993 (2017).

    Article 
    ADS 

    Google Scholar
     

  • Okada, C. et al. A 50.1-Mpixel 14-bit 250-frames/s back-illuminated stacked CMOS image sensor with column-parallel kT/C-canceling S&H and ΔΣADC. IEEE J. Solid-State Circuits 56, 3228–3235 (2021).

    Article 
    ADS 

    Google Scholar
     

  • Solhusvik, J. et al. 1280 × 960 2.8 μm HDR CIS with DCG and split-pixel combined. In Proc. International Image Sensor Workshop 254–257 (2019).

  • Murakami, H. et al. A 4.9 Mpixel programmable-resolution multi-purpose CMOS image sensor for computer vision. In 2022 IEEE International Solid-State Circuits Conference (ISSCC) 104–106 (IEEE, 2022).

  • iniVation. DAVIS 346, https://inivation.com/wp-content/uploads/2019/08/DAVIS346.pdf (iniVation, 2019).

  • Kandel, E. R., Koester, J. D., Mack, S. H. & Siegelbaum, S. A. Principles of Neural Science 4th edn (McGraw-Hill, 2000).

  • Mishkin, M., Ungerleider, L. G. & Macko, K. A. Object vision and spatial vision: two cortical pathways. Trends Neurosci. 6, 414–417 (1983).

    Article 

    Google Scholar
     

  • Jähne, B. EMVA 1288 Standard for machine vision: Objective specification of vital camera data. Optik Photonik 5, 53–54 (2010).

    Article 

    Google Scholar
     

  • Reda, F. A. et al. FILM: Frame Interpolation for Large Motion. In Proc. IEEE/CVF International Conference on Computer Vision 250–266 (ACM, 2022).

  • Woo, S., Park, J., Lee, J.-Y. & Kweon, I. S. CBAM: Convolutional Block Attention Module. In Proc. European Conference on Computer Vision (ECCV) 3–19 (2018).

  • Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference Proc. Part III Vol. 18 (eds Navab, N. et al.) 234–241 (Springer, 2015).

  • Ranjan, A. & Black, M. J. CBAM: Convolutional Block Attention Module. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 4161–4170 (CVF, 2018).

  • Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You Only Look Once: unified, real-time object detection. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 779–788 (IEEE, 2016).

  • Wu, D. et al. YOLOP: You Only Look Once for Panoptic Driving Perception. Mach. Intell. Res. 19, 550–562 (2022).

    Article 

    Google Scholar
     

  • Bochkovskiy, A., Wang, C.-Y. & Liao, H.-Y. M. YOLOv4: optimal speed and accuracy of object detection. Preprint at https://arxiv.org/abs/2004.10934 (2020).

  • Horn, B. K. & Schunck, B. G. Determining optical flow. Artif. Intell. 17, 185–203 (1981).

    Article 

    Google Scholar
     

  • Wang, T. Tianmouc dataset. Zenodo https://doi.org/10.5281/zenodo.10602822 (2024).

  • Wang, T. Code of “A vision chip with complementary pathways for open-world sensing”. Zenodo https://doi.org/10.5281/zenodo.10775253 (2024).

  • iniVation. Understanding the Performance of Neuromorphic Event-based Vision Sensors White Paper (iniVation, 2020).

  • iniVation. DAVIS 346 AER https://inivation.com/wp-content/uploads/2023/07/DAVIS346-AER.pdf (iniVation, 2023).


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