1. Purpose and scope
An AI accelerator is only as good as its fit to the workloads it runs. This stage turns a business idea, such as faster training of large language models in data centres or efficient inference in edge devices, into a precise, agreed set of models and workloads that every later decision is measured against. It ends when that set is chosen, characterised and agreed.
The stage covers which model families matter, which phases of their use matter (training, fine tuning, inference, or a mix), the data types and accuracy each tolerates, batch sizes and latency targets customers expect, and how work is spread across many chips. It does not choose an architecture. Its job is to state the problem so clearly that architecture options can be compared fairly.
Because models change faster than a chip takes to build, the stage also records the assumptions the team is making about where models are going, so they can be revisited at later gates rather than discovered too late.
12 more sections for registered members.