Note: each student is required to choose 1-2 paper(s) from this list and present in class (presentation schedule TBD). Students are NOT expected to read all papers in this list — only papers selected by student presenters will be discussed (typically 2 papers per class).
Core Ideas
- 1.[Howard 2017]
Google AI
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Arxiv - 2.[Sandler 2018]
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. - 3.[Zhang 2017]
Megvii
Shufflenet: An extremely efficient convolutional neural network for mobile devices
Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. - 4.[Ma 2018]
Megvii
Shufflenet v2: Practical guidelines for efficient cnn architecture design
Proceedings of the European conference on computer vision (ECCV). 2018. - 5.[Hubara 2018]
Quantized neural networks: Training neural networks with low precision weights and activations
Journal of Machine Learning Research, 2018 - 6.[Jain 2020]
Efficient Execution of Quantized Deep Learning Models: A Compiler Approach - 7.[Guo 2018]
A survey on methods and theories of quantized neural networks
Arxiv - 8.[Bonawitz 2019]
Towards federated learning at scale: System design - 9.[Newell 1975]
Computer science as empirical inquiry: Symbols and search. In ACM Turing award lectures (p. 1975).
Programming Languages
- 10.[David 2020]
Tensorflow lite micro: Embedded machine learning on tinyml systems
Arxiv
Tools
- 11.[Chen 2018]
TVM: end-to-end optimization stack for deep learning
OSDI,18 - 12.[Banbury 2021]
Micronets: Neural network architectures for deploying tinyml applications on commodity microcontrollers
Proceedings of the 4th MLSys Conference - 13.[Cyphers 2018]
Intel ngraph: An intermediate representation, compiler, and executor for deep learning
Arxiv - 14.[Chen 2020]
Experiments and optimizations for TVM on RISC-V Architectures with P Extension - 15.[Lugaresi 2019]
MediaPipe: A Framework for Building Perception Pipelines - 16.[Jiang 2020]
Mnn: A universal and efficient inference engine
Platforms
- 17.[Wang 2019]
Benchmarking TPU, GPU, and CPU Platforms for Deep Learning - 18.[Yazdanbakhsh 2021]
An evaluation of edge tpu accelerators for convolutional neural networks - 19.[Reuther 2019]
Survey and benchmarking of machine learning accelerators
HPEC, IEEE, 2019 - 20.[Osman 2021]
TinyML Platforms Benchmarking
Applications in Embedded AI
- 21.[Metwaly 2019]
Edge Computing with Embedded AI: Thermal Image Analysis for Occupancy Estimation in Intelligent Buildings
Proceedings of the INTelligent Embedded Systems Architectures and Applications Workshop 2019 - 22.[Mazzia 2020]
Real-Time Apple Detection System Using Embedded Systems With Hardware Accelerators: An Edge AI Application
IEEE Access 8 (2020): 9102-9114. - 23.[Wang 2019]
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
IEEE Network 33.5 (2019): 156-165. - 24.[Zhang 2020]
Mediapipe hands: On-device real-time hand tracking - 25.[Castro-Zunti 2020]
License plate segmentation and recognition system using deep learning and OpenVINO
IET Intelligent Transport Systems 14.2 (2020): 119-126. - 26.[Xu 2019]
A First Look at Deep Learning Apps on Smartphones
The World Wide Web Conference. 2019. - 27.[Rahman 2021]
An Internet-of-Medical-Things-Enabled Edge Computing Framework for Tackling COVID-19
IEEE Internet of Things Journal 8.21 (2021): 15847-15854. - 28.[Xu 2019]
SCEH: Smart Customized E-Health Framework for Countryside Using Edge AI and Body Sensor Networks
2019 IEEE global communications conference (GLOBECOM). IEEE, 2019. - 29.[Guo 2020]
FEEL: A Federated Edge Learning System for Efficient and Privacy-Preserving Mobile Healthcare
49th International Conference on Parallel Processing-ICPP. 2020. - 30.[Nahian 2020]
Towards Artificial Intelligence Driven Emotion Aware Fall Monitoring Framework Suitable for Elderly People with Neurological Disorder
International Conference on Brain Informatics. Springer, Cham, 2020. - 31.[Lin 2021]
Intelligent warehouse monitoring based on distributed system and edge computing
International Journal of Intelligent Robotics and Applications (2021): 1-13. - 32.[Zhao 2020]
IoT edge computing-enabled collaborative tracking system for manufacturing resources in industrial park
Advanced Engineering Informatics 43 (2020): 101044. - 33.[Chen 2019]
iRAF: A Deep Reinforcement Learning Approach for Collaborative Mobile Edge Computing IoT Networks
IEEE Internet of Things Journal 6.4 (2019): 7011-7024. - 34.[Xu 2019]
A computation offloading method over big data for IoT-enabled cloud-edge computing
Future Generation Computer Systems 95 (2019): 522-533. - 35.[Zhang 2020]
STEC-IoT: A Security Tactic by Virtualizing Edge Computing on IoT
IEEE Internet of Things Journal 8.4 (2020): 2459-2467. - 36.[Singh 2020]
Hierarchical Security Paradigm for IoT Multiaccess Edge Computing
IEEE Internet of Things Journal 8.7 (2020): 5794-5805. - 37.[Nieves-Acaron 2021]
ACE: An ATAK Plugin for Enhanced Acoustic Situational Awareness at the Edge
MILCOM 2021-2021 IEEE Military Communications Conference (MILCOM). IEEE. - 38.[Wyatt 2021]
Environmental Sound Classification with Tiny Transformers in Noisy Edge Environments
2021 IEEE 7th World Forum on Internet of Things (WF-IoT). IEEE, 2021. - 39.[Elliott 2020]
Cyber-Physical Analytics: Environmental Sound Classification at the Edge
2020 IEEE 6th World Forum on Internet of Things (WF-IoT). IEEE, 2020. - 40.[Shen 2021]
The analysis of intelligent real‑time image recognition technology based on mobile edge computing and deep learning
Journal of Real-Time Image Processing 18.4 (2021): 1157-1166. - 41.[Gia 2019]
Edge AI in Smart Farming IoT: CNNs at the Edge and Fog Computing with LoRa
2019 IEEE AFRICON. IEEE, 2019. - 42.[Zhang 2021]
Trustworthy Target Tracking With Collaborative Deep Reinforcement Learning in EdgeAI-Aided IoT
IEEE Transactions on Industrial Informatics 18.2 (2021): 1301-1309. - 43. [Li, Tian 2020]
Federated Learning: Challenges, Methods, and Future Directions
IEEE Signal Processing Magazine 37.3 (2020): 50-60 - 44.[Frank 2003]
Tuning of the Structure and Parameters of a Neural Network Using an Improved Genetic Algorithm
IEEE Transactions on Neural networks 14.1 (2003): 79-88. - 45.[Cheng 2017]
A Survey of Model Compression and Acceleration for Deep Neural Networks
arXiv preprint arXiv:1710.09282 (2017).