AI Systems Architecture — Ch.08: Experiments and Benchmarks [KR]

System benchmarking evaluates AI architecture performance, efficiency, scalability, and reliability in both controlled and real environments, differing from model‑level evaluation that focuses on predictive accuracy. Hardware selection determines AI system performance, scalability, reliability, and operational efficiency by translating application requirements into measurable hardware specifications, considering workload, memory capacity, and data movement. Hardware benchmarking provides systematic methods to assess AI hardware platforms' computational capability, efficiency, and operational characteristics, enabling meaningful comparisons of CPUs, GPUs, TPUs, NPUs, FPGAs, and specialized accelerators for training and inference.