ECE 410/510 “Hardware for AI and ML” is offered again in the spring term!

This course covers the design, simulation, optimization, and evaluation of specialized hardware for artificial intelligence and machine learning workloads. Students begin with the mathematical foundations that connect AI/ML algorithms to their hardware implementations, then apply HW/SW co-design methods to map algorithms such as CNNs, DNNs, and transformer-based LLMs onto architectures including GPUs, TPUs, FPGAs, systolic arrays, and neuromorphic processors. Additional topics include computational profiling with Python and CUDA, benchmarking across hardware platforms, in-memory computing with memristive crossbar arrays, and the use of LLMs as hardware design tools for HDL generation and physical design automation. The course alternates between lectures and hands-on codefest sessions where students tackle open-ended design challenges, from implementing neurons and systolic sorting to running simulations on neuromorphic hardware platforms such as BrainScaleS-2.

More info here.