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In Section 3 we address the delicate issue of implementing the algorithm with a reduced amount of memory, since, with a large number of items and large data coefficients, space requirements can constitute a problem.
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In the context of the first scenario, H.264/AVC using the proposed prediction architecture reduced the amount of memory by 31.6 and 51.9% while it speeded-up coding by on average of 14 and 57%, compared to the same codec deploying an extended architecture based on 3D-DMB and hierarchical B-picture, respectively.
This change reduced the amount of memory required for the curation application running in the client's browser.
Reducing the number of reads can dramatically reduce the amount of memory needed during the assembly process.
In this case, two-stage table mapping should be employed to reduce the amount of memory.
To reduce the amount of memory usage, an address generation unit was used.
We have tested three versions with loop-fusion in order to increase the AI ratio by reducing the amount of memory accesses.
The structure of the decoder is modified to reduce the amount of memory of the input buffer, the metric memory, the interleaver and the deinterleaver though it is based on the SW-MAP scheme.
The Baseline Bloom Filters lookup algorithm we developed is built on top of the Naïve version and includes optimizations to (i) reduce the amount of memory used in each Bloom filters structure and to (ii) decrease the number of Bloom filters employed.
Alternatively, when a 1D filter is used, it is possible to perform an operation with five 128-bit registers for a horizontal filter and three 128-bit registers for a vertical filter; therefore, the use of a separable filter also makes it possible to reduce the amount of memory access.
The size of images is 1280 × 960 pixels as mentioned in Section 2. The captured image is resized into 640 × 480 pixels in order to reduce the amount of memory usage and the computation time, assuming that this algorithm is implemented on embedded systems.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com