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In recent years, code compression has been frequently investigated for embedded systems to reduce memory use and power consumption.
These methods typically reduce memory use at the expense of the computation time needed for pattern searches.
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Our proposed embedding is inspired by 1-of-m embedding, but achieves a much smaller, denser representation reducing memory use, network input layer size and training time.
The results demonstrate our new character embedding approach, denoted as log-m, greatly reduces training time by 4.85 to 5.75× and reduces memory use by up to a factor of 16.
First, we explain our new character embedding approach and demonstrate that it greatly reduces memory use and network training time due to greatly reducing the size of the initial input received by the network.
To reduce memory usage, we used small integers for scoring and greedy affine gap penalty calculations (Supplementary Material).
Therefore in order to reduce memory consumption, we chose to use 40 nucleotides in the PDEGEM.
2. Contigs are assembled on a graph of unique k-mers and paired SNP k-mers sampled to reduce memory usage, then ordered and oriented using the Bambus scaffolder [ 26, 46].
RNA-Skim again uses bloom filters to reduce memory usage.
A commonly used solution for reducing memory usage is provided by calculating a running mean [10], using a forgetting factor.
Furthermore, we use a coverage map to reduce memory footprint used for the temporal shadow map.
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