Dynamic Structural and Computational Resource Allocation for Self-Organizing Architectures

Efficient dynamic allocation of computation resources can enable significant improvements in terms of performance and/or power consumption of hardware architectures. In this paper, we propose three bio-inspired mechanisms for self-organizing cellular hardware architecture built on top of the Cellular Self-Organizing Map (CSOM) vector quantization algorithm. Taking inspiration from the self-organization present in living organisms, we make use of structural and functional plasticity through the modulation of synapses and neurons usage. SPCSOM extends CSOM through synapse pruning and sprouting, yielding a SOM that can dynamically modulate its topology throughout its lifetime. MSPCSOM adds a neuron migration method between different clusters of the map. SBMCSOM deals with multiple SOMs handling different tasks by dynamically reassigning neurons between the tasks, seeking a trade-off between the quantization error of each SOM. Algorithms are intended to run in SCALP, a multi-FPGA hardware platform for prototyping self-organizing networks.