Optimized implementation of digital signal processing applications with gapless data acquisition

Abstract This paper presents novel models and design optimization methods for gapless deep waveform applications, where continuous streams of data must be processed reliably without dropping any samples. The approaches developed in this paper involve unified dataflow-based modeling of the interfaces and signal processing functionality of gapless deep waveform analysis. Bottleneck actors (computational modules) in the resulting dataflow model are then identified and tackled with approximate computing techniques. These techniques are developed and configured carefully so that large performance gains are achieved while keeping reductions in signal processing accuracy to a manageable level. Efficient actor- and graph-level code optimization techniques are also applied to further improve real-time performance. In addition to providing accurate, real-time processing on the experimental platform used in our experiments, the algorithm- and model-based formulation of the contributions in this part promotes their general utility in deep waveform analysis and their retargetability to other platforms.