The relentless pursuit of artificial intelligence innovation hinges critically on the underlying hardware infrastructure. As AI models grow in complexity, scale, and computational demands, general-purpose central processing units (CPUs) increasingly become bottlenecks, struggling to efficiently execute the massive parallel computations inherent in modern machine learning workloads. This inadequacy has driven the development and widespread adoption of specialized hardware accelerators, meticulously engineered to optimize AI performance, delivering unprecedented
