Bank and bank group conflicts are major performance bottlenecks for memory intensive workloads. Idealized experiments show removing bank and bank group conflicts collectively can improve performance by up to 37.5% and by 22.5% on average for our mix of multi-programmed memory intensive workloads. We propose the Duplicon Cache to mitigate bank and bank group conflict penalties by duplicating select lines of data to an alternative bank group, giving the memory controller the freedom to source the data from the bank group which avoids conflicts. The Duplicon Cache is entirely implemented in the memory controller and does not require changes to commodity memory. We identify and address the main challenges associated with duplication: 1) tracking duplicated data efficiently, 2) identifying which data to duplicate, and 3) replacing stale duplicated data while protecting useful ones. Our evaluations show the Duplicon Cache configured with 128MB of storage (out of 16GB of main memory) improves performance by 8.3% while reducing energy by 5.6%.

This paper examines techniques for normalization of unseen speakers in recognition. Two implementations of linear spectrum warping were examined: time domain resampling and filter bank scaling. It is shown that for seen speakers, the models trained by unwarped utterances are less sensitive to spectrum warping by filter bank scaling than by resampling. A pitch-based scheme for warping factor estimation has been proposed. The method is shown to be cost-effective in reducing the variability of unseen speakers compared to the ML-based methods. In particular the combination of filter bank scaling with the pitch-based warping factor estimation reduces the error rate of isolated Mandarin digit recognition by more than 30% for unseen speakers.