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Futures: Custom AI Hardware Platforms for High Schoolers
The course then introduces the Application Specific Integrated Circuit (ASIC) and explores the design pipeline from prototyping hardware-based ML algorithms with FPGAs to production in ASICs. Then, neuromorphic computing, the technology inspired by the human brain, will be introduced to examine a system view of the combination of hardware and software components in the implementation of AI tasks and ML algorithms. The course, and series, concludes with a Capstone project, where students will apply their knowledge by implementing an AI/ML algorithm on custom hardware, gaining hands-on experience in designing, optimizing, and deploying system-level (hardware and software) solutions.
Futures: AI on CPU and GPU Platforms for High Schoolers
“AI on CPU and GPU platforms” provides an in-depth examination of Central Processing Unit (CPU) and Graphics Processing Unit (GPU) architectures and their role in AI and Machine Learning (ML) model execution. The course covers techniques in optimizing ML models for CPUs, focusing on computational efficiency and power management.? Then, GPU architectures are introduced and justified as better suited for parallel processing certain AI tasks and Machine Learning algorithms. The course also covers strategies for optimizing machine learning models on GPUs, leveraging the unique strengths of these architectures. Additionally, an introduction to custom GPU programming constructs will be presented, enabling the development of tailored code for performance enhancements in ML and AI applications. By the end of the course, students will be equipped to fine-tune AI models for both CPU and GPU platforms, understanding the trade-offs in performance and efficiency.
Futures: Introduction to AI Hardware for High Schoolers – UC San Diego
The course also provides a comprehensive review of the essential Digital Design techniques as well as key Machine Learning algorithm concepts, reinforcing knowledge from the prerequisite Digital Circuit Design and Machine Learning series to understand the foundational elements of digital hardware and AI software.? The course then introduces computer architecture principles, laying the groundwork for hardware architectures that are utilized today to enhance AI system performance. By the end of the course, students will be equipped with the foundational knowledge in digital design and machine learning algorithms to advance to more focused AI hardware topics in the subsequent courses of the series.
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