The rapid proliferation of artificial intelligence across industries has intensified the scrutiny on underlying hardware infrastructure. Organizations grappling with deploying AI models, from intricate deep learning networks to sophisticated natural language processing, inevitably face a pivotal decision: whether to leverage widely available, off-the-shelf AI accelerators or to embark on the ambitious journey of developing custom AI chips. This choice profoundly impacts performance, cost, time-to-market, power
