AI accelerators — tensor cores, systolic arrays, spatial architectures — are designed for matrix multiplications. Scientific computing also uses matrix operations but requires different numerical patterns: sparse linear algebra, iterative solvers, stencil computations. Can AI hardware do general numerics?
The authors (arXiv:2603.23343) implement numerical kernels and a conjugate gradient solver on Tenstorrent's Wormhole spatial architecture. The spatial design — where data flows between processing elements without centralized control — maps naturally onto stencil operations and iterative algorithms where each element communicates with neighbors.
The through-claim: AI accelerators and scientific computing share the same fundamental operation (matrix computation) but diverge in access patterns and control flow. Spatial architectures bridge this gap better than GPU architectures because the spatial data flow matches the locality structure of scientific computations. The hardware designed for neural network layers turns out to be well-suited for PDE solvers — not because the problems are similar, but because both are local computations over regular grids.