Leveraging Power Spectral Density for Scalable System-Level Accuracy Evaluation

Abstract : The choice of fixed-point word-lengths critically impacts the system performance by impacting the quality of computation, its energy, speed and area. Making a good choice of fixed-point word-length generally requires solving an NP-hard problem by exploring a vast search space. Therefore, the entire fixed-point refinement process becomes critically dependent on evaluating the effects of accuracy degradation. In this paper, a novel technique for the system-level evaluation of fixed-point systems, which is more scalable and that renders better accuracy, is proposed. This technique makes use of the information hidden in the power-spectral density of quantization noises. It is shown to be very effective in systems consisting of more than one frequency sensitive components. Compared to state-of-the-art hierarchical methods that are agnostic to the quantization noise spectrum, we show that the proposed approach is 5× to 500× more accurate on some representative signal processing kernels.
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Benjamin Barrois, Karthick Parashar, Olivier Sentieys. Leveraging Power Spectral Density for Scalable System-Level Accuracy Evaluation. IEEE/ACM Conference on Design Automation and Test in Europe (DATE), Mar 2016, Dresden, Germany. pp.6. ⟨hal-01253494⟩

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