A tour of accelerator architectures for AI/ML applications
In collaboration with Mason Nelson, I gave a lecture on the necessity, architectural foundations, academic research, and industry trends of accelerators for AI/ML applications. This lecture was given as part of the EECS 573 Microarchitecture course at the University of Michigan, taught by Prof. Todd Austin.
Learning objectives for this lecture include:
- Characterize common operations in Machine Learning that can be accelerated
- Understand why Machine Learning is an inefficient task for general-purpose processors and why accelerators are necessary
- Become acquainted with various architecture paradigms, both historical and contemporary, to AI accelerator design
- Review current academic research and commercial products that implement the basic architecture paradigms
- Reflect on possible next steps in accelerator design
Please reach out to me if you are interested in accessing the recording or the slides for this talk.