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PhiNets: A Scalable Backbone for Low-power AI at the Edge
Abstract
In the Internet of Things era, where we see many interconnected and heterogeneous mobile and fixed smart devices, distributing the intelligence from the cloud to the edge has become a necessity. Due to limited computational and communication capabilities, low memory and limited energy budget, bringing artificial intelligence algorithms to peripheral devices, such as end-nodes of a sensor network, is a challenging task and requires the design of innovative solutions. In this work, we present PhiNets , a new scalable backbone optimized for deep-learning-based image processing on resource-constrained platforms. PhiNets are based on inverted residual blocks specifically designed to decouple the computational cost, working memory, and parameter memory, thus exploiting all available resources for a given platform. With a YoloV2 detection head and Simple Online and Realtime Tracking (SORT), the proposed architecture achieves state-of-the-art results in (i) detection on the COCO and VOC2012 benchmarks, and (ii) tracking on the MOT15 benchmark. PhiNets obtain a reduction in parameter count of around 90% with respect to previous state-of-the-art models (EfficientNetv1, MobileNetv2) and achieve better performance with lower computational cost. Moreover, we demonstrate our approach on a prototype node based on an STM32H743 microcontroller (MCU) with 2 MB of internal Flash and 1MB of RAM and achieve power requirements in the order of 10 mW. The code for the PhiNets is publicly available on GitHub. 1
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
(2016) · Cited by 31,590
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128
YOLO9000: Better, Faster, Stronger
Redmon, J., & Farhadi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.690 · Cited by 19,892
YOLOv4: Optimal Speed and Accuracy of Object Detection
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020) · arXiv · doi:10.48550/arxiv.2004.10934 · Cited by 10,307
Simple online and realtime tracking with a deep association metric
Wojke, N., Bewley, A., & Paulus, D. (2017) · 2017 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2017.8296962 · Cited by 5,286
Simple online and realtime tracking
Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016) · 2016 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2016.7533003 · Cited by 4,433
CenterNet: Keypoint Triplets for Object Detection
Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., & Tian, Q. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00667 · Cited by 4,079
The PASCAL visual object classes challenge 2006 (VOC2006) results
Everingham, M., Zisserman, A., Williams, C. K. I., & Gool, L. V. (2006) · Cited by 2,079
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498 · cited 3×
“TinyML has been applied to several different classes of problems and devices, such as audio processing and sound event detection [6, 38], biosignals processing [21], gesture recognition [43], and general time series data [15].”
BackgroundGAP-8: A RISC-V SoC for AI at the Edge of the IoT
Flamand, É., Rossi, D., Conti, F., Loi, I., Antonio Pullini, D., Rotenberg, F., & Luca Benini, C. (2018) · IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP) · doi:10.1109/asap.2018.8445101 · Cited by 216
PULP-NN: accelerating quantized neural networks on parallel ultra-low-power RISC-V processors
Garofalo, A., Rusci, M., Conti, F., Rossi, D., & Benini, L. (2019) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2019.0155 · Cited by 192
XNOR-Net++: Improved Binary Neural Networks
Bulat, A., & Tzimiropoulos, G. (2019) · arXiv · doi:10.48550/arxiv.1909.13863 · Cited by 165
Machine Learning on Mainstream Microcontrollers
Sakr, F., Bellotti, F., Berta, R., & De Gloria, A. (2020) · Sensors · doi:10.3390/s20092638 · Cited by 109
Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Codesign
Hao, C., Dotzel, J., Xiong, J., Benini, L., Zhang, Z., & Chen, D. (2021) · IEEE Design and Test · doi:10.1109/mdat.2021.3069952 · Cited by 54
A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors
John, A., Cardiff, B., & John, D. (2021) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas51556.2021.9401300 · Cited by 44
Neural Network Distillation on IoT Platforms for Sound Event Detection
Cerutti, G., Prasad, R., Brutti, A., & Farella, E. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2394 · Cited by 30
Self-citationMemory-Efficient Speech Recognition on Smart Devices
Venkatesh, G., Valliappan, A., Mahadeokar, J., Shangguan, Y., Fuegen, C., Seltzer, M. L., & Chandra, V. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414502 · Cited by 9
Always-ON visual node with a hardware-software event-based binarized neural network inference engine
Rusci, M., Rossi, D., Flamand, É., Gottardi, M., Farella, E., & Luca Benini, C. (2018) · ACM International Conference on Computing Frontiers (CF) · doi:10.1145/3203217.3204463 · Cited by 9
Self-citationRomaszkan, W., Li, T., & Gupta, P. (2020) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3371157 · Cited by 7
People/Car Classification using an Ultra-Low-Power Smart Vision Sensor
Paissan, F., Cerutti, G., Gottardi, M., & Farella, E. (2019) · International Workshop on Advances in Sensors and Interfaces (IWASI) · doi:10.1109/iwasi.2019.8791337 · Cited by 6
Self-citation
Flagged first, then by citations
Research works printed but not linked: 17
- Han Cai Chuang Gan and Song Han. 2019. Once for all: Train one network and specialize it for efficient deployment. arXiv:1908.09791. Retrieved from https://arxiv.org/abs/1908.09791. · arXiv
- Han Cai Chuang Gan Ligeng Zhu and Song Han. 2020. Tiny transfer learning: Towards memory-efficient on-device learning. arXiv:2007.11622. Retrieved from https://arxiv.org/abs/2007.11622. · arXiv
- Xiaoliang Dai Peizhao Zhang Bichen Wu Hongxu Yin Fei Sun Yanghan Wang Marat Dukhan Yunqing Hu Yiming Wu Yangqing Jia Peter Vajda Matt Uyttendaele and Niraj K. Jha. 2018. ChamNet: Towards efficient network design through platform-aware model adaptation. arXiv:1812.08934. Retrieved from https://arxiv.org/abs/1812.08934. · arXiv
- M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. [n. d.]. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. Retrieved from http://www.pascal-network.org/challenges/VOC/voc2012/ workshop/index.html.
- Shayan Hassantabar Prerit Terway and Niraj K. Jha. 2020. TUTOR: Training neural networks using decision rules as model priors. arXiv:2010.05429. Retrieved from https://arxiv.org/abs/2010.05429. · arXiv
- Shayan Hassantabar Zeyu Wang and Niraj K. Jha. 2019. SCANN: Synthesis of compact and accurate neural networks. arXiv:1904.09090. Retrieved from https://arxiv.org/abs/1904.09090. · arXiv
- Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861. Retrieved from https://arxiv.org/abs/1704.04861. · arXiv
- Liangzhen Lai Naveen Suda and Vikas Chandra. 2018. Cmsis-nn: Efficient neural network kernels for arm cortex-m cpus. arXiv:1801.06601. Retrieved from https://arxiv.org/abs/1801.06601. · arXiv
- Laura Leal-Taixé Anton Milan Ian Reid Stefan Roth and Konrad Schindler. 2015. Motchallenge 2015: Towards a benchmark for multi-target tracking. arXiv:504.01942. Retrieved from https://arxiv.org/abs/504.01942.
- Ji Lin Wei-Ming Chen Yujun Lin John Cohn Chuang Gan and Song Han. 2020. Mcunet: Tiny deep learning on iot devices. arXiv:2007.10319. Retrieved from https://arxiv.org/abs/2007.10319. · arXiv
- Francesco Paissan Massimo Gottardi and Elisabetta Farella. 2021. Enabling energy efficient machine learning on a Ultra-Low-Power vision sensor for IoT. arXiv:2102.01340. Retrieved from https://arxiv.org/abs/2102.01340. · arXiv
- Shaoqing Ren Kaiming He Ross Girshick and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv:1506.01497. Retrieved from https://arxiv.org/abs/1506.01497. · arXiv
- Mingxing Tan and Quoc Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In Proceedings of the International Conference on Machine Learning. PMLR, 6105–6114.
- Mingxing Tan and Quoc V. Le. 2021. Efficientnetv2: Smaller models and faster training. arXiv:2104.00298. Retrieved from https://arxiv.org/abs/2104.00298. · arXiv
- Dianlei Xu Tong Li Yong Li Xiang Su Sasu Tarkoma Tao Jiang Jon Crowcroft and Pan Hui. 2020. Edge intelligence: Architectures challenges and applications. arXiv:2003.12172. Retrieved from https://arxiv.org/abs/2003.12172. · arXiv
- Yifu Zhang Chunyu Wang Xinggang Wang Wenjun Zeng and Wenyu Liu. 2020. FairMOT: On the fairness of detection and re-identification in multiple object tracking. arXiv:2004.01888. Retrieved from https://arxiv.org/abs/2004.01888. · arXiv
- Andy Zhou Rikky Muller and Jan M. Rabaey. 2021. Memory-efficient limb position-aware hand gesture recognition using hyperdimensional computing. arXiv:2103.05267. Retrieved from https://arxiv.org/abs/2103.05267. · arXiv
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Metadata
- Authors
- Paissan, F., Ancilotto, A., & Farella, E.
- Affiliations
- Fondazione Bruno Kessler (IT)
- Venue
- ACM Transactions on Embedded Computing Systems
- Published
- 2022-09-30
- Volume
- 21
- Issue
- 5
- Pages
- 1-18
- ISSN
- 1539-9087 (ISSN-L)
- 1558-3465
- OpenAlex
- W4210269029
- Cited by
- 54
- References
- 25
- Citations by year
- 2026: 13 · 2025: 15 · 2024: 15 · 2023: 5 · 2022: 5
- FWCI
- 3.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Embedded Computing Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- edge AI, low-power deep learning, resource-constrained devices, inverted residual blocks, lightweight neural networks, object detection, YOLOv2, multi-target tracking, Internet of Things, energy efficiency, parameter reduction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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