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Compared with the model in [30, 33], GAC can achieve much better tradeoff between memory and computational complexity.
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The potential for all memory-centric designs is the bandwidth between memory and the nearest computational unit.
Such an improvement reduces the memory and computational efforts.
The encoding algorithms have low memory and computational requirements.
However, they are limited in power, memory, and computational capacities.
Error correction requires additional memory and computational power.
A frequency reduction is thus necessary [26] to cope with memory and computational restrictions.
All these simulations required different memory and computational effort leading to differing run times.
Through these models, we can set boundaries on memory and computational requirements.
In this case, the CUSUM approach needs less memory and computational resources.
The main drawbacks of this algorithm are its considerable requirements for memory and computational time [18].
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