New course teaches how AI models run on modern accelerators

Most engineers building AI models today work on GPUs. They write models in PyTorch, run them, and measure latency and utilization. What happens between the framework and the silicon often remains hidden. That was manageable when one architecture dominated. It is much harder now, as AI hardware has diversified across GPUs, Google TPUs, the Cerebras Wafer-Scale Engine (WSE), the Groq Language Processing Unit (LPU), and IBM’s Spyre accelerator.

A new Coursera specialization, Dataflow Architectures for AI: From Principles to Practice, is built for engineers who need to work across that range. Taught by Kaoutar El Maghraoui, a principal research scientist at IBM Research and adjunct professor of computer science at Columbia University, the six-course program covers execution models, compilation, memory systems, debugging, and performance modeling through hands-on labs.

“Sometimes the model itself isn’t the difficult part,” said El Maghraoui. “The difficult part is understanding how that…