TL;DR: Why Dataflow Architecture Matters for AI Inference
AI inference is a data movement problem, not a compute problem. The bottleneck in modern inference isn't arithmetic speed. It's how many unnecessary trips data makes to memory. Faster chips alone don't fix this.
GPUs pay a penalty on every token. Traditional kernel-by-kernel execution writes intermediate results out to memory and fetches them back for every operation. In the decode phase, that penalty compounds with every single token generated.
Dataflow eliminates the handoffs. By fusing operations into a continuous pipeline and keeping intermediate data local on-chip, Dataflow Architecture removes the stop-start boundaries that slow GPU inference down.
The three-tier memory hierarchy is an extension of the same idea. SRAM handles the hottest local work, HBM streams model weights at scale, and DDR supports prompt caching and multi-model workflows. Each tier is matched to the job it does best.
