Reconfigurable computing architectures combine the flexibility of software with the performance of custom hardware. Such architectures are of particular interest at the nanoscale level. We argue that a bottom-up self-assembled fabric of nodes will be easier and cheaper to manufacture, however, one has to make compromises with regards to the device regularity, homogeneity, and reliability. The goal of this thesis is to evaluate the performance and cost of a self-configurable computing architecture composed of simple reconfigurable nodes for unstructured and unknown fabrics. We built a software and hardware framework for this purpose. The framework enables creating an irregular network of compute nodes where each node can be configured as a simple 2-input, 4-bit logic gate. The compute nodes are organized hierarchically by sending a packet through a top anchor node that recruits compute nodes with a chemically-inspired algorithm. The nodes are then self-configured by means of a gate-level netlist describing any digital logic circuit. A topology-agnostic optimization algorithm inspired by simulated annealing is then initiated to self-optimize the circuit for latency. Latency comparisons between non-optimized, brute-force optimized and our optimization algorithm are made. We further implement the architecture in VHDL and evaluate hardware cost, area, and energy consumption. The simple on-chip topology-agnostic optimization algorithm we propose results in a significant (up to 50\%) performance improvement compared to the non-optimized circuits. Our findings are of particular interest for emerging nano and molecular-scale circuits.