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) |