The paper describes a slice-based data-allocation strategy for tree-based topologies. The approach is supported by a theoretical analysis demonstrating the optimality of the data-distribution procedure. According to its basic principle of operation, data is split in such a way that at run time no processor ever stands idle. The benefits of this approach are quite important in several practical applications, including high-dimensional data processing and neural network modeling. Experimental results obtained from a noise-like coding model of associative memory confirm the validity of the overall approach.
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