ELEC 515 Embedded Machine Learning - Schedule
Schedule Notes:
Section 1: Introduction of commonly used ML algorithms
Section 2: State-of-the-art algorithmic techniques of embedded ML
Section 3: State-of-the-art ML accelerator architectures and chips
| Date | Topic |
| Section 1: Introduction of commonly used ML algorithms | |
| 8/26/2019 (Mon.) | Background, Overview, and Logistics |
| 8/28/2019 (Wed.) | DNN Inference and Training; Introduction to Google Colab |
| 9/2/2019 (Mon.) | Labor Day (No Scheduled Classes) |
| 9/4/2019 (Wed.) | Introduction to State-of-the-art DNNs |
| 9/9/2019 (Mon.) | Datasets, and Benchmarking Metrics; Deep Learning Software |
| 9/11/2019 (Wed.) | Decision Tree; Random Forests |
| 9/16/2019 (Mon.) | AdaBoost; Support Vector Machine Learning |
| 9/18/2019 (Wed.) | Generative Adversarial Network |
| 9/23/2019 (Mon.) | Reinforcement Learning |
| 9/25/2019 (Wed.) | Recurrent Neural Network; kNN, k-Means |
| 9/30/2019 (Mon.) | Summary of Section I |
| Section 2: State-of-the-art algorithmic techniques of embedded ML | |
| 10/2/2019 (Wed.) | How to Use Embedded Machine Learning Tools: Mobiles and FPGAs (Invited Speaker: Yue Wang and Pengfei Xu from Rice) |
| 10/7/2019 (Mon.) | Quantization Basics, and a Case Study: HAQ: Hardware-Aware Automated Quantization with Mixed Precision (CVPR 2019) (Invited Speaker: Zhijian Liu from MIT) |
| 10/9/2019 (Wed.) | Quantized DNNs, and its Case Study: 8-bit Floating Point Training (NeurIPS 2018) (Invited Speaker: Dr. Naigang Wang from IBM) |
| 10/14/2019 (Mon.) | Midterm Recess (No Scheduled Classes) |
| 10/16/2019 (Wed.) | Unstructured and Structured Pruning, and its Case Study: To Prune, or Not to Prune (ICLR 2018) |
| 10/21/2019 (Mon.) | Compression vis Early Exiting, Prediction, and its Case Studies. |
| 10/23/2019 (Wed.) | Compression via Parameter Sharing, Indexing, and its Case Study: Deep k-Means (ICML 2018) |
| 10/28/2019 (Mon.) | Regularization and Case Study of Unstructured Pruning: Learning Structured Sparsity in DNNs (NIPS 2016) (Invited Speaker: Wei Wen from Duke) |
| 10/30/2019 (Wed.) | Compression via Transferred/Compact Convolutional Filters, and its Case Study: SqueezeNet ; Compression via Knowledge Distallastion, and its Case Study: Distilling the Knowledge in a Neural Network |
| 11/4/2019 (Mon.) | Compression via Neural Architecture Search, and its Case Study: FBNet (Invited Speaker: Dr. Bichen Wu from Berkeley) |
| Section 3: State-of-the-art ML accelerator architectures and chips | |
| 11/6/2019 (Wed.) | Overview of DNN Acelerator Development, and DNN Accelerator Simulators/Chip-predictors |
| 11/11/2019 (Mon.) | Algorithm to Architecture Mapping I |
| 11/13/2019 (Wed.) | Algorithm to Architecture Mapping II |
| 11/18/2019 (Mon.) | Pipelined and Parallelled Processing as Low Power Techniques; Near Threshold Computing |
| 11/20/2019 (Wed.) | Energy-Delay TradeOff; DNN Accelerator Case Study (Eyeriss) |
| 11/25/2019 (Mon.) | DNN Accelerator Case Study (Google TPU, IBM TrueNorth, and DianNao series) |
| 11/27/2019 (Wed.) | Analog Processing Basics; DNN Accelerator Case Study: Towards artificial general intelligence with hybrid Tianjic chip architecture (Invited Speaker: Dr. Lei Deng from UCSB) |
| 12/2/2019 (Mon.) | no class |
| 12/4/2019 (Wed.) | Processing-in-Memory Basics; DNN Accelerator Case Study: CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices (Invited Speaker: Dr. Yanzhi Wang from Northeastern University) |
| 12/5/2019 (Thur.) | Course Project Presentation (15 mins Pres + 5 mins Q&A) |