Project Introduction: AI System (AISys)
The AI System project, also known as AISys, is a comprehensive and open-source educational initiative designed to explore and learn about the system design of artificial intelligence and deep learning. This initiative is spearheaded by ZOMI, who has accumulated extensive experience in constructing full-stack AI systems throughout their career. AISys invites AI enthusiasts and professionals to engage in meaningful discussions, promoting learning and collaboration within the AI community. The project is continually updated and available on multiple platforms including AISys website, Bilibili, YouTube, and GitHub.
Background and Framework
AISys is focused on the design and development of AI systems, offering insights into both foundational and advanced aspects of AI architecture. With a vision to support learners ranging from senior undergraduates to AI system practitioners, AISys provides resources to thoroughly understand the architectural frameworks that underpin AI technologies. The curriculum is methodically structured into five key areas, each focusing on different aspects of AI systems:
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AI Full-stack Overview: Provides foundational knowledge and an overview of AI systems, including system design methodologies and the architecture of training and inference.
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AI Chip and Architecture: Delves into the hardware aspect of AI, covering the fundamentals of CPUs, GPUs, and specialized AI chips. It examines their design principles and application environments.
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AI Programming and Computational Architecture: Focuses on AI programming from a system design perspective, addressing compiler issues essential for modern machine learning systems.
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AI Inference Systems and Engines: Concentrates on real-world applications and the systems that enable AI inference, sharing essential practices and algorithms.
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Core Technologies of AI Frameworks: Discusses the technologies fundamental to AI frameworks, including automatic differentiation and neural network representations.
Educational Objectives
AISys seeks to achieve the following educational goals:
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Enable learners to gain a comprehensive understanding of AI's computational architecture.
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Introduce leading-edge system architectures combined with AI through research and development insights.
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Equip learners with knowledge of mainstream frameworks, platforms, and tools essential for AI systems.
Detailed Course Sections
I. AI System Overview
- This section provides a synthesized understanding of integrating algorithms, frameworks, and architecture to form cohesive AI systems.
II. AI Chip Architecture
- Students explore the computation models and foundational principles of AI technologies, including an in-depth look at various processors such as CPUs, GPUs, and NPUs.
III. AI Compilation Principles
- The course covers traditional compilers and modern AI compiler development, addressing front-end and back-end optimizations vital for AI compilation.
IV. AI Inference Systems
- Offers insights into real-world inference systems, focusing on business applications and the practical considerations of deploying AI engines.
V. Core Technologies of AI Frameworks
- Features a deep dive into critical AI framework technologies, such as automatic differentiation and the current trends in distributed training for large models.
AISys is continually evolving, with ongoing updates to its curriculum and resources. It remains an essential resource for those seeking to deepen their understanding of AI systems and their practical applications.