Meta Latent Computation

We introduce a spectral decomposition framework for computational workloads and prove that the von Neumann architecture — where all hardware resources serve a single universal execution mode — is strictly suboptimal for any non-degenerate workload. A computational workload \(w\) is decomposed into \(N\) fundamental modes \(\{\phi_k\}\) with weights \(\{c_k\}\); an architecture \(A\) is a resource allocation \(\{r_k\}\) across these modes subject to a budget constraint \(\sum_k r_k \leq R\). Given mode-specific throughput \(\{s_k\}\), the throughput functional \(\eta(A, w) = \sum_k c_k \, s_k \, r_k\) is maximized by the latent-optimal allocation \(r_k^ \propto \sqrt{c_k \, s_k}\). We prove that \(\eta(A^, w) > \eta(A_{\text{vN}}, w)\) for all workloads with at least two active modes and at least one mode where specialized hardware outperforms general-purpose hardware — a condition satisfied by every practical workload. Benchmarks on seven canonical workloads (Monte Carlo simulation, deep learning, graph analytics, scientific computing, database queries, mixed general, pure sequential) demonstrate speedups of 3×–216× over the von Neumann baseline. Real machine measurements on Apple Silicon confirm the predicted spectral structure. The framework provides a formal optimality criterion for heterogeneous computing that the von Neumann architecture, GPU offload, and current SoC designs can be evaluated against.

Keywords: von Neumann architecture, heterogeneous computing, spectral decomposition, resource allocation, computational efficiency, latent theory