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Since memristive devices are especially tiny to be densely packed in crossbar-like structures and possess long time memory needed by neuromorphic synapses, this paper shows how to approximate the behavior of synapses in neural networks using this memristive device.
A first reason to explain the relatively low speedup is the amount of memory needed by the application.
Hence, LIDE with mixture model would require roughly one seventh of memory needed by integral image based AHE.
First, we estimate the memory needed by our data structure, under the assumption of an infinite number of bitmaps.
Thus, the amount of memory needed by the algorithm depends on the number of parents inferred by the network.
These restrictions can be approximately considered as using 1000th, 1%, 10% and 100% of memory needed by GA*.
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Moreover, storing the kernel can be another issue as the amount of memory needed is cursed by the dimensionality of the system.
Because the fractional equation is more effective for the height model, the memory needed can be reduced by 1.3 million array elements in the LUT method.
The space complexity is mainly determined by the memory needed for the data related to the OvGraph and temporary data used at the preprocessing step.
Then, we present a technique to reduce the memory needed to store the control law by borrowing ideas from algebraic decision diagrams for compact function representation and by exploiting the non-determinism of the synthesized controllers.
For example, creating a dense stoichiometric matrix with 4000 reactions takes approximately 100MB of computer memory and various matrix operations may increase the actual memory need by a factor of ten.
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