EE6625 - Hardware Architectures for Artificial Intelligence and Machine Learning | ||||||||
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| * The offering term is subject to change without prior notice | ||||||||
Course Aims | ||||||||
This course provides an introduction of how developments in deep learning algorithms have influenced hardware design advancements in recent time. It discusses bottlenecks of traditional computer architecture and introduces new paradigms including compute-in-memory and neuromorphic computing, as well as developments in conventional computing architectures and specialized machine learning accelerators. Also, create algorithm-hardware co-design strategies for creating and refining hardware specifically for deep learning algorithms will be developed by the students. | ||||||||
Assessment (Indicative only, please check the detailed course information) | ||||||||
Continuous Assessment: 50% | ||||||||
Examination: 50% | ||||||||
Examination Duration: 2 hours | ||||||||
Min. Continuous Assessment Passing Requirement: 30% | ||||||||
Min. Examination Passing Requirement: 30% | ||||||||
To pass the course, students are required to achieve at least 30% in course work and 30% in the examination. | ||||||||
Detailed Course Information | ||||||||
| EE6625.pdf | ||||||||
