機器學習晶片架構設計
Accelerator Architectures for Machine Learning
| 節 | 週二 |
|---|---|
7 15:30–16:20 | 機器學習晶片架構設計 ED302（光復） 3 節連堂 |
8 16:30–17:20 | |
9 17:30–18:20 |
* 根據陽明交大上課時間表所列
Machine learning has captured tremendous successes to solve difficult learning problems. Hardware accelerators pursue continued performance and energy-efficient gains to meet the intensive computation in machine learning applications. This course explores leading approaches that tackle machine learning computational challenges and have been emerged in industrial and academic research. This course aims to build up students a foundation to understand the programming and accelerator architectural functions. This course begins with the fundamental basis of deep neural networks (DNN). The second potion of this course provides students accelerator hardware architectures specified for machine learning workloads. This course will address the graphic processing units (GPUs) that are widely used for the training of the neural networks and specialized machine learning accelerators such as tensor processor units (TPUs). The final portion of this course discusses challenges in designing accelerator architectures for machine learning applications and introduces emerging accelerator architectures. This course includes the programming assignments to use the computer architecture simulator, research paper reading and a class project to reflect ideas that improve accelerator architecture designs.
Computer architecture and digital logic circuit design
無備註
class website:https://people.cs.nctu.edu.tw/~ttyeh/course/2022_Fall/IOC5009/outline.html
10 % paper reading 40 % homework and lab assignments 20% midterm exam 30 % class project
DNN Models
Popular DNN models DNN Kernel Computation DNN Quantization DNN Sparsity
GPU
GPU programming GPU architecture
DNN accelerators
Data flow DNN accelerator Near/In Memory Processing HW-SW Co-Design
| 週次 | 主題 |
|---|---|
| 第 1 週 | Class Organization & Foundations of Deep Learning 2023-09-12(二) |
| 第 2 週 | DNN Methods and Models 2023-09-19(二) |
| 第 3 週 | DNN Kernel Computation 2023-09-26(二) |
| 第 4 週 | DNN Data Type Quantization 2023-10-03(二) |
| 第 5 週 | DNN Sparsity 2023-10-10(二) |
| 第 6 週 | Sparse DNN Accelerators 2023-10-17(二) |
| 第 7 週 | GPU Programming Model and Instruction Set Architecture 2023-10-24(二) |
| 第 8 週 | GPU SIMT Core architecture 2023-10-31(二) |
| 第 9 週 | GPU Memory System 2023-11-07(二) |
| 第 10 週 | Introduction to GPGPU-Sim Simulator 2023-11-14(二) |
| 第 11 週 | Machine Learning GPU Kernel Optimization 2023-11-21(二) |
| 第 12 週 | DNN Dataflow Accelerators Part I 2023-11-28(二) |
| 第 13 週 | DNN Dataflow Accelerators Part II 2023-12-05(二) |
| 第 14 週 | DNN Benchmarking (MLPerf) 2023-12-12(二) |
| 第 15 週 | DNN HW-SW Co-design (Model Pruning) 2023-12-19(二) |
| 第 16 週 | DNN Near/In Memory Processing 2023-12-26(二) |
| 第 17 週 | Advanced Technology for Accelerated ML 2024-01-02(二) |
| 第 18 週 | Conclusion 2024-01-09(二) |
1. Efficient Processing of Deep Neural Networks, Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, Joel S. Emer, Synthesis Lectures on Computer Architecture, Morgan & Claypool, 2020 2. Deep Learning for Computer Architects, Brandon Reagen, Robert Adolf, Paul Whatmough, Gu-Yeon Wei, and David Brooks, Synthesis Lectures on Comput-er Architecture, Morgan & Claypool, 2017 3. General-Purpose Graphics Processor Architectures, Tor M. Aamodt, Wilson Wai Lun Fung, and Timothy G. Rogers, Synthesis Lectures on Computer Archi-tecture, Morgan & Claypool, 2018 4. Programming Massively Parallel Processors: A Hands-on Approach, Kirk, D.B., & Hwu, W.M.W., 3rd Edition, Elsevier, Inc., 2016.
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