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Towards Energy-Efficient Hyperspectral Image Processing Inside Camera Pixels
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“Because of the absence of wireless connectivity in remote regions, they proposed to use an offline model that achieves 96% accuracy while conserving battery power.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [85]
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
Deep learning-based classification of hyperspectral data
Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2329330 · Cited by 2,679
Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016) · arXiv · doi:10.48550/arxiv.1602.02830 · Cited by 2,106
Zhong, Z., Li, J., Luo, Z., & Chapman, M. A. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2755542 · Cited by 2,033
HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification
Roy, S. K., Krishna, G., Dubey, S. R., & Chaudhuri, B. B. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2918719 · Cited by 2,010
Adão, T., Hruška, J., Pádua, L., et al. (2017) · Remote Sensing · doi:10.3390/rs9111110 · Cited by 1,319
Ultrafast machine vision with 2D material neural network image sensors
Mennel, L., Symonowicz, J., Wachter, S., Polyushkin, D. K., Molina‐Mendoza, A. J., & Mueller, T. (2020) · Nature · doi:10.1038/s41586-020-2038-x · Cited by 1,180
3-D Deep Learning Approach for Remote Sensing Image Classification
Hamida, A. B., Benoît, A., Lambert, P., & Amar, C. B. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2818945 · Cited by 950
Review of snapshot spectral imaging technologies
Hagen, N. A., & Kudenov, M. W. (2013) · Optical Engineering · doi:10.1117/1.oe.52.9.090901 · Cited by 746
Chi, P., Li, S., Xu, C., Zhang, T., Zhao, J., Liu, Y., Wang, Y., & Xie, Y. (2016) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca.2016.13 · Cited by 644
Hyperspectral Image Classification With Deep Feature Fusion Network
Song, W., Shutao Li, A., Fang, L., & Lu, T. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2794326 · Cited by 586
Evaluating the Energy Efficiency of Deep Convolutional Neural Networks on CPUs and GPUs
Li, D., Chen, X., Becchi, M., & Zong, Z. (2016) · 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom) · doi:10.1109/bdcloud-socialcom-sustaincom.2016.76 · Cited by 236
Zheng, Z., Zhong, Y., Ma, A., & Zhang, L. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2967821 · Cited by 232
HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image
Luo, Y., Zou, J., Yao, C., Zhao, X., Li, T., & Bai, G. (2018) · International Conference on Audio, Language and Image Processing · doi:10.1109/icalip.2018.8455251 · Cited by 183
Visual Wake Words DatasetPreprint
Chowdhery, A., Warden, P., Shlens, J., Howard, A., & Rhodes, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05721 · Cited by 170
Automatic 3D Reconstruction from Multi-date Satellite Images
Facciolo, G., de Franchis, C., & Meinhardt-Llopis, E. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.198 · Cited by 142
Kundu, S., Datta, G., Pedram, M., et al. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00400 · Cited by 118
Self-citationAn In-Memory VLSI Architecture for Convolutional Neural Networks
Kang, M., Lim, S., Gonugondla, S. K., & Shanbhag, N. R. (2018) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2018.2829522 · Cited by 66
Chen, Z., Zhu, H., Ren, E., Liu, Z., Jia, K., Luo, L. Z., Zhang, X., Wei, Q., Qiao, F., Liu, X.-J., et al. (2019) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2019.2937227 · Cited by 63
A Reconfigurable Convolution-in-Pixel CMOS Image Sensor Architecture
Song, R., Huang, K., Wang, Z., et al. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3179370 · Cited by 50
Modeling and Enhancement of Piezoelectric Accelerometer Relative Sensitivity
Reguieg, S. K., Ghemari, Z., Benslimane, T., et al. (2018) · Sensing and Imaging · doi:10.1007/s11220-018-0222-y · Cited by 42
Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding
Datta, G., Kundu, S., & Beerel, P. A. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534306 · Cited by 38
Self-citationCan Deep Neural Networks be Converted to Ultra Low-Latency Spiking Neural Networks?
Datta, G., & Beerel, P. A. (2022) · Design, Automation, and Test in Europe · doi:10.23919/date54114.2022.9774704 · Cited by 37
Self-citationDeep Residual Involution Network for Hyperspectral Image Classification
Meng, Z., Zhao, F., Liang, M., & Xie, W. (2021) · Remote Sensing · doi:10.3390/rs13163055 · Cited by 34
Fundamental Limits on Energy-Delay-Accuracy of In-Memory Architectures in Inference Applications
Gonugondla, S. K., Sakr, C., Dbouk, H., & Shanbhag, N. R. (2021) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2021.3124757 · Cited by 27
Real-time image processing for augmented reality on mobile devices
Lv, Z., Lloret, J., & Song, H. H. (2021) · Journal of Real-Time Image Processing · doi:10.1007/s11554-021-01097-9 · Cited by 20
Datta, G., Kundu, S., Yin, Z., Mathai, J., Liu, Z., Wang, Z., Tian, M., Lu, S., Lakkireddy, R. T., Schmidt, A., et al. (2022) · IFIP/IEEE International Conference on Very Large Scale Integration · doi:10.1109/vlsi-soc54400.2022.9939582 · Cited by 19
Self-citationA CMOS-Compatible, Monolithically Integrated Snapshot-Mosaic Multispectral Imager
Gonzalez, P., Geelen, B., Blanch, C., et al. (2015) · NIR news · doi:10.1255/nirn.1531 · Cited by 18
Dynamic Temperature Management of Near-Sensor Processing for Energy-Efficient High-Fidelity Imaging
Kodukula, V., Katrawala, S., Jones, B., et al. (2021) · Sensors · doi:10.3390/s21030926 · Cited by 17
Detailed characterization of a mosaic based hyperspectral snapshot imager
Hahn, R., Hämmerling, F.-E., Haist, T. J., et al. (2020) · Optical Engineering · doi:10.1117/1.oe.59.12.125102 · Cited by 16
Fully Embedding Fast Convolutional Networks on Pixel Processor Arrays
Bose, L., Dudek, P., Chen, J., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58526-6_29 · Cited by 11
P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML ApplicationsPreprint
Datta, G., Kundu, S., Yin, Z., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1459821/v1 · Cited by 6
Self-citationDatta, G., Kundu, S., Jaiswal, A., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.11979 · Cited by 5
Self-citationP2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML ApplicationsPreprint
Datta, G., Kundu, S., Yin, Z., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2203.04737
Self-citationDatta, G., Kundu, S., Yin, Z., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.14285
Self-citation
Research works printed but not linked: 2
- Sony to release world’s first intelligent vision sensors with AI processing functionality (2020). https://www.sony.com/en/SonyInfo/News/Press/202005/20-037E/. Accessed 12 Jan 2022
- Alipour-Fard, T., Paoletti, M.E., Haut, J.M., Arefi, H., Plaza, J., Plaza, A.: Multibranch selective kernel networks for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 1(1), 1–5 (2020)
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A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366
Metadata
- Authors
- Datta, G., Yin, Z., Jacob, A. P., Jaiswal, A. R., & Beerel, P. A.
- Affiliations
- University of Southern California (US)
- Corresponding author
- Datta, Gourav
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Pages
- 303-316
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4321353513
- Cited by
- 5
- References
- 36
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 3
- FWCI
- 2.35
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Funders
- Defense Advanced Research Projects Agency (HR00112190120)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › CCD and CMOS Imaging Sensors
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Keywords
- hyperspectral image analysis, energy efficiency, convolutional neural networks, data compression, bandwidth reduction, hyperspectral image classification
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