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On that basis, according to McKinsey, the total cost of the data center functions would be $366 a month per unit of computing output, compared with $150 a month for the conventional data center.
In the present work a systematic methodology for computing output stabilizing feedback control laws for nonlinear systems subject to saturating inputs is presented.
The proposed results are motivated from its use for designing minimal-order observers and for computing output feedback control laws by a particular technique.
Algorithms for computing output sensitivities to parameter variances, model evidence and posterior model plausibilities for given data, and for computing what are referred to as Occam Categories in reference to a rough measure of model simplicity, make up components of the overall approach.
This is moving into the realm of full size computing output, while still maintaining a small amount of power usage.
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To compute output error performance objectives, the EBB equation is discretized via finite differences in space and time and reformulated to a state space system.
In the second step, we compute output functionals of the solution, such as the Radar Cross Section (RCS), independently of the dimension of the discretization space, for many different parameter values in a many-query context at very little cost.
Part of solution verification consists in comparing computed output with analytic solutions (so called "benchmark solutions").
Thus in a conventional butterfly, to compute output X, two floating point adders/subtractors are required.
Indeed, for each applied entry, the filter function is executed, in order to compute output samples.
To compute output Y, four floating point adders/subtractors and four floating point multipliers are required.
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