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Class Schedule (Subject to Change)

Date Discussion Topics and Papers
W1 – 1.24 Course introduction and logistics
W2 – 1.31 Fundamentals of Embedded Systems and AI/ML
 W3 – 2.7

TinyML (Vijay Janapa Reddi, Harvard)

paper 1: A Survey of Model Compression and Acceleration for Deep Neural Networks [Yunfan Cai]

paper 2: MobileNetV2: Inverted Residuals and Linear Bottlenecks [Sara Sun]

paper 3: A First Look at Deep Learning Apps on Smartphones [Wenpu Wang]

paper 4: IoT edge computing-enabled collaborative tracking system for manufacturing resources in industrial park [Runfeng Huang]

 W4 – 2.14

Hardware AI/ML Accelerators (Zhangxi Tan, RIOS Lab)

paper 1: Experiments and optimizations for TVM on RISC-V Architectures with P Extension [Xunqi Li]

paper 2: Real-Time Apple Detection System Using Embedded Systems With Hardware Accelerators: An Edge AI Application [Sophie Liu]

paper 3: TinyML Platforms Benchmarking [Yuqi Zhu]

paper 4: An evaluation of edge tpu accelerators for convolutional neural networks [Botong Xiao]

 W5 – 2.21

Embedded Data (Jorge Ortiz, Rutgers)

paper 1: Quantized neural networks: Training neural networks with low precision weights and activations [Baizhou (David) Hou]

paper 2: Survey and benchmarking of machine learning accelerators [Chuan-Tung Lin]

paper 3: License plate segmentation and recognition system using deep learning and OpenVINO [Perry Flamer]

paper 4: Shufflenet v2: Practical guidelines for efficient cnn architecture design [Kevin Jiang]

 W6 – 2.28

Federated Learning and Mobile AI (Nic Lane, Cambridge)

paper 1: TVM: end-to-end optimization stack for deep learning [Kaiyuan Hou]

paper 2: Environmental Sound Classification with Tiny Transformers in Noisy Edge Environments [Srivatsan]

paper 3: Towards federated learning at scale: System design [Lanxiang Hu]

paper 4: FEEL: A Federated Edge Learning System for Efficient and Privacy-Preserving Mobile Healthcare [Yousuf Baker]

 W7 – 3.7

Scalable AI (Matt Welsh, OctoML)

paper 1: MediaPipe: A Framework for Building Perception Pipelines [Yuxin Wang]

paper 2: A survey on methods and theories of quantized neural networks [Yifan Zhan]

paper 3: Shufflenet: An extremely efficient convolutional neural network for mobile devices [Manisha Rajkumar]

paper 4: Efficient Execution of Quantized Deep Learning Models: A Compiler Approach [Qingxin Cheng]

 W8 – 3.14 Spring break (no class)
 W9 – 3.21

Tutorial: TensorFlow Lite Micro

paper 1: Micronets: Neural network architectures for deploying tinyml applications on commodity microcontrollers [Dewei Wang]

paper 2: Tensorflow lite micro: Embedded machine learning on tinyml systems [Hanshan Li]

paper 3: Mobilenets: Efficient convolutional neural networks for mobile vision applications [Yinsen Jia]

paper 4: Intel ngraph: An intermediate representation, compiler, and executor for deep learning [Junyu Li]

 W10 – 3.28

Tutorial: TVM/uTVM (Gavin Uberti, OctoML) 

paper 1: Benchmarking TPU, GPU, and CPU Platforms for Deep Learning [Angel Estigarribia]

paper 2: Mnn: A universal and efficient inference engine [Isha Garg]

paper 3: Trustworthy Target Tracking With Collaborative Deep Reinforcement Learning in EdgeAI-Aided IoT [Aashi Kapoor]

paper 4: Tuning of the Structure and Parameters of a Neural Network Using an Improved Genetic Algorithm [Shangru Li]

 W11 – 4.4

Tutorial: TensorFlow Lite Micro

paper 1: Federated Learning: Challenges, Methods, and Future Directions [Mandy Zhong]

paper 2: Mediapipe hands: On-device real-time hand tracking [Yunfan Gao]

paper 3: Edge Computing with Embedded AI: Thermal Image Analysis for Occupancy Estimation in Intelligent Buildings [Angzi Xu]

paper 4: Intelligent warehouse monitoring based on distributed system and edge computing [Chaoyu Fan]

 W12 – 4.11

Acoustic AI (Stephen Xia) 10:10am to 11:00am

Entrepreneurship (Thomas Cheng, Brava) 11:00am to 11:30am

paper 1: The analysis of intelligent real‑time image recognition technology based on mobile edge computing and deep learning [Richard Samoilenko]

paper 2: Edge AI in Smart Farming IoT: CNNs at the Edge and Fog Computing with LoRa [Abhijeet Nayak]

paper 3: A computation offloading method over big data for IoT-enabled cloud-edge computing [Johanan Sowah]

paper 4: Computer Science as Empirical Enquiry: Symbol and Search [Muneer Khan]

paper 5: STEC-IoT: A Security Tactic by Virtualizing Edge Computing on IoT [Shuai Zhang]

W13 – 4.18

Embedded AI in Structures (Shijia Pan, UC Merced)

paper 1: Cyber-Physical Analytics: Environmental Sound Classification at the Edge [Longyi Li]

paper 2: SCEH: Smart Customized E-Health Framework for Countryside Using Edge AI and Body Sensor Networks [Jeongwook Lee]

paper 3: An Internet-of-Medical-Things-Enabled Edge Computing Framework for Tackling COVID-19 [Spandan Das]

paper 4: iRAF: A Deep Reinforcement Learning Approach for Collaborative Mobile Edge Computing IoT Networks [Krishan Kumar]

W14 – 4.25

Federated Learning in Health AI (Robert Dickerson, Babylon Health)

paper 1: Towards Artificial Intelligence Driven Emotion Aware Fall Monitoring Framework Suitable for Elderly People with Neurological Disorder [Shibo Sheng]

paper 2: Hierarchical Security Paradigm for IoT Multiaccess Edge Computing [Sakshi Gulgulia]

paper 3: ACE: An ATAK Plugin for Enhanced Acoustic Situational Awareness at the Edge [Rui Chu]

paper 4: In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning [Junyi Wu]

W15 – 5.2 Project Gala

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