Objective
With the rapid development and increasingly application of Artificial Intelligence (AI) technology, specific AI Integrated Circuits (IC) are required to provide powerful and efficient computing capability, which drives the needs in market for types of AI IC suitable for different application situation. Therefore, this course intends to provide the enrolled students with the typical technology, system architecture and design methodology for AI IC design. Moreover, software tool chains are also introduced. This course is to support and enable the students with improved capability to design AI IC.
The content of this course is composed of five sections: 1) Fundamentals of AI and typical algorithms; 2) Technology and system architecture of AI IC, as well as design methodology considering algorithm, software and hardware; 3) Software tool chains related to the application of AI IC; 4) Emerging semiconductor devices, computing modality, circuit design, system architecture, requirement and trend; 5) Practice of AI IC design. The students to be enrolled in this course are required to be with essential knowledge in AI algorithm, software and IC design.
人工智能技術的飛速發展和日益廣泛的應用，需要專門的人工智能芯片提供強大、高效的計算能力，因此形成了對適用不同應用場景的多型人工智能芯片的市場需求。有鑒於此，這門課程引導參加此課程的學生學習人工智能芯片的技術和系統架構、算法軟件硬件相結合的設計方法、以及相關的工具鏈，以使能學生提高設計人工智能芯片和相關軟件工具鏈的能力。
在本課程中學生將學習五部分的知識：1）人工智能基礎和代表性算法；2）人工智能芯片的技術基礎、芯片架構、算法軟件硬件相結合的設計方法；3）人工智能芯片相關的工具鏈；4）新興的半導體器件、計算方法、芯片設計、系統架構、需求與趨勢；5）人工智能芯片設計實踐。參加本課程的學生需要具有基本的人工智能算法知識、軟件知識和芯片設計知識。
Syllabus
Introduction to artificial intelligence IC
Fundamentals of AI algorithms
Characteristics of AI computing
Architectures of AI IC – Overview
Architectures of AI IC – Operator
Architectures of AI IC – Data flow
Architectures of AI IC – Advance
Design methodology of AI IC
Tool chains of AI IC
Emerging devices, architectures and trend
Learning Outcome
Upon successful completion of this course, students will be able to:
1. Understand the introductory knowledge and typical algorithms of AI computing.
2. Understand the typical techniques and representative architectures of AI IC.
3. Understand the Domain-Specific Architecture (DSA) design methodology including algorithm, hardware and software.
4. Understand the software tools for the application of AI IC.
5. Understand emerging semiconductor devices, system architecture, requirement and trend.
6. Obtain practical experience of system development including requirement analysis, algorithm optimization, IC architecture and module design.
