ECE 498NS/598NS: Deep Learning in Hardware
Lecture Notes
2019/8/27: Introduction [ PDF ]
2019/8/29: Deep Learning - An Introduction [ PDF ]
2019/9/3: Reducing DNN Complexity via Quantizaion [ PDF ] [ Python Notebook ]
2019/9/5: Quantizaion [ PDF ] [ Python Notebook ]
2019/9/10: Fixed-point DNNs [ PDF ]
2019/9/12: Low Complexity DNNs [ PDF ]
2019/9/17 & 2019/9/19: DNN Training - I [ PDF ]
2019/9/24: DNN Training - II [ PDF ] [ Python Notebook ]
2019/9/26 & 2019/10/1: DNN Training - III [ PDF ] [ Python Notebook ]
2019/10/3: Low-complexity DNNs - Learned Quantization & Model Compression [ PDF ]
2019/10/10: DNN Architectures – Roofline, Data Reuse & Systolic Architectures [ PDF ]
2019/10/15: DNN Architectures – Accelerators Case Studies I [ PDF ]
2019/10/17: DNN Architectures – Accelerators Case Studies II [ PDF ]
2019/10/22 & 2019/10/24: Algorithm Transforms [ PDF ]
2019/10/29: Energy Delay Trade Offs [ PDF ]
2019/10/31 & 2019/11/5: Benchmarking Methodology [ PDF ]
2019/11/7: Statistical Error Compensation [ PDF ]
2019/11/12: Project Overview [ ECE598NSG Project Description ] [ ECE598NSU Project Description ] [ PYNQ Tutorial ] [ Model files ]
2019/11/14 & 2019/11/19: The Deep In-memory Architecture [ PDF ]
2019/11/21 & 2019/12/3: DIMA Case Studies [ PDF ]
2019/12/5: IMC Case Studies [ PDF ]
2019/12/10: Future Directions [ PDF ]
Lecture Notes from Fall 2017 Offering
Introduction [PDF]
The LMS Algorithm and Architecture [PDF]
Fixed-Point LMS [PDF]
Algorithm-to-Architecture Mapping Techniques [PDF]
Energy-Delay Trade-offs [PDF]
Logistic Regression, ADALINE, and Perceptron [PDF]
The Support Vector Machine [PDF]
Training via the Stochastic Gradient Descent Algorithm [PDF]
Boosting and Random Forest [PDF]
Deep Learning [PDF]
DianNao Case Study [PDF]
Introduction to Shannon-inspired Statistical Computing and Algorithmic Noise Tolerance [PDF]
Beyond Algorithmic Noise Tolerance [PDF]
Deep In-Memory Computing [PDF]
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