The escalating demands of artificial intelligence, particularly deep learning, have profoundly reshaped the landscape of computing hardware. Traditional Central Processing Units (CPUs), designed for general-purpose sequential tasks and diverse workloads, are increasingly proving to be a bottleneck for the intricate, highly parallel computations inherent in modern AI. While CPUs excel at complex control logic and broad instruction sets, their architecture is fundamentally ill-suited for the massive matrix multiplications and convolutions that form the bedrock of neural network operations. This inherent inefficiency – characterized by limited arithmetic logic units (ALUs) and an emphasis on low-latency single-thread performance – has driven the rapid evolution and adoption of specialized AI accelerators.
The initial wave of AI acceleration was spearheaded by Graphics Processing Units (GPUs). Originally conceived for rendering complex 3D graphics, GPUs possess an intrinsically parallel architecture, featuring thousands of simpler processing cores optimized for simultaneous execution of identical operations on different data (Single Instruction, Multiple Data – SIMD). NVIDIA, through its CUDA programming model and relentless innovation in GPU design, effectively democratized high-performance parallel computing for researchers. This pivotal