Expert Modules

Master the techniques for profiling, characterizing, and optimizing deep learning workloads across different hardware platforms

Strategic simulation methodology: choose the right simulation paradigm and fidelity level; ask targeted questions, validate against reality

Translate simulated activity into power/thermal behavior and communicate perf/W trade-offs credibly using McPAT and HotSpot

Master Performance, Power, and Area analysis techniques for evaluating hardware design trade-offs in AI accelerators

Master queueing theory fundamentals and advanced techniques for analyzing performance, tail latency, and congestion control in modern computer systems, from CPU pipelines to datacenter-scale networks

Cut simulation time while bounding error and preserving phase behavior through systematic sampling methodologies

Turn counters and simple models into clear diagnoses and action items using systematic performance analysis methodologies

Cross-validate models with real counters, quantify uncertainty, and communicate limits in performance analysis