Offer a productized architecture evaluation service for AI accelerator teams. The first engagement would take a target model family and candidate accelerator architecture, build or adapt workload models, run mapping and performance experiments, and deliver a ranked tradeoff report with design recommendations. Over time, the service can become a reusable benchmarking harness, simulator adapter library, and workload mapping methodology for accelerator teams.

Large AI workloads are pushing companies toward custom accelerators, analog in-memory computing, TPUs, and data-center inference/training hardware. The cost of poor architecture choices is rising as models, memory pressure, and energy constraints become central design limits.