Signal processing with nodal networks on a SIMD massively parallel processor

A discussion of nodal network methodologies that can be used to effectively implement algorithms that range from signal processing to discrete logic systems in a large-scale single-instruction multiple-data (SIMD) parallel processor such as the Connection Machine (CM) is presented. As a first step, two versions of an algorithm to track formats in speech are implemented. The first implementation uses data parallel coding techniques, the second implementation uses a nodal network. The algorithm contains logic that, at every frequency/time point in a spectrogram, chooses between several filters to find the filter that best matches linear energy structure at that point. The choice of filter at each point is determined on the basis of information in adjacent points. The nodal network implementation of the algorithm uses only two node types, a fuzzy AND and a fuzzy OR. The connections between nodes can be either noninverting or inverting. The inverting effectively produces a NOT. The algorithm relies on the parameters associated with each node and connectivity between the nodes to simulate the original algorithm. The result is a nodal network programmed to identify formats in a spectrogram. The two implementations are comparable in performance and speed of execution