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Different feature extraction methods are studied for retaining temporal information, and for dimensionality reduction at each branch of the hierarchy.
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This method (TC-HyOM) aggregates two different approaches for each branch of the hierarchy, namely, the instruction and data branches.
Independent analyses in terms of performance and energy consumption may be done for each branch of the hierarchy (instruction cache branch or data cache branch).
This work evaluates several methods intended for two-level hierarchies, and the results showed that when we combine different methods for each branch of the hierarchy, results can be improved.
Therefore, by querying each branch of the hierarchy, different color segment of an image can be obtained separately.
Each DRCN along with all their children nodes form one branch of the hierarchy.
Then if a criterion is updated, all meta-criteria in the same branch of the hierarchy are also updated.
Hierarchies are extremely valuable in curation, as they allow associated data to be viewed at various levels of granularity, with data annotated to children of a branch to be aggregated at each higher level of the hierarchy.
The Ward's hierarchical clustering method applied to TA-mean datasets generally paralleled the non-hierarchical AP method, if we examine branches of the hierarchy at a depth of two or three from the root.
This kind of hierarchy allows us to evaluate instruction and data caches branches separately, although previous approaches have applied the same method for both branches of the hierarchy.
Table 7 Number of simulations comparison for complete execution of different applications in both branches of the hierarchy considering different optimization mechanisms.
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