Synthetic Computing: Non-Biological Evolutionary Computation Beyond the Von Neumann Model

For over seven decades, the von Neumann model has guided digital computation, enabling deterministic, high-precision systems operating on static instruction sets. However, emerging computational demands—autonomy, adaptation, non-linear optimization, real-time learning, and large-scale uncertainty—exceed the expressive capacity of architectures rooted in fixed logic and centralized control. This paper develops a conceptual framework for synthetic computing: non-biological, non-silicon, evolution-driven substrates capable of self-modification, emergent behavior, and adaptive complexity. Unlike organic or neuromorphic systems, synthetic architectures do not mimic biological tissues; instead, they establish a computational ontology grounded in artificial evolutionary physics. We examine existing partial attempts (reconfigurable logic, evolutionary algorithms, in-memory compute, analog accelerators), identify structural limitations, and outline theoretical principles for fully adaptive, non-Von-Neumann computation.