The relentless growth of artificial intelligence, particularly deep learning, has exposed fundamental limitations in traditional computing architectures. General-purpose CPUs, designed for sequential processing, struggle with the massive parallelism inherent in neural network computations. While GPUs have served as workhorses for AI training due to their parallel processing capabilities, their architecture, optimized for graphics rendering, still carries overhead not directly relevant to AI tasks. The demand for ever
