Accelerating AI through Novel Compute Architecture and Digital Photonic Gates

The growing computational demands and performance limits of AI models are often constrained by their underlying mathematical processing units. This issue spans various model sizes and hardware---from laptop CPUs to large GPU clusters. Current units, like a CPU's Floating-Point Unit (FPU), use multi-cycle pipelines. While this design supports complex computations, it also adds latency and slows floating-point performance. Spending multiple cycles on an operation is slow, but preferable to lowering the global clock frequency to support these intricate units.