@inproceedings{bar-par-sen-16-aa-powspec,
  author = {Barrois, Benjamin and Parashar, Karthick and Sentieys, Olivier},
  title = {Leveraging Power Spectral Density for Scalable System-Level Accuracy Evaluation},
  booktitle = {Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE)},
  year = 2016,
  month = mar,
  doi = {10.3850/9783981537079_0204},
  pages = {750-755},
  comment = {Mentions AA. Perhaps only en passant?},
  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 5x to 500x more accurate on some representative signal processing kernels.}
}