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The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.
In this section, we investigate the performance of the high-speed hardware architecture for multivariate scheme through hardware implementations on an Altera Stratix FPGA.
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Implementations on a sounding rocket flight, parabolic flights and drop tower shots are analyzed.
We present several implementations on a representative range of MIMD computers that include shared memory multiprocessors, distributed virtual shared memory computers, as well as networks of workstations.
Strong corners are determined and used as flow points using implementations on a graphic processing unit (GPU), resulting in significant speed-up over central processing units (CPU).
We parallelize Myers algorithm using different MapReduce implementations on a multi-core CPU.
Not only is this implementation better than four other considered methods, it also outperforms FABS-SVM and FABS-spectral implementations on a mitochondria databases.
The computation time required by the implementations on a desktop PC appears feasible, being maximally a few hours for MCMC and a few minutes for GEM.
To describe the strategy's effect in the long-term, measured in both arms one year after the end of implementation, on an index day.
We describe a preliminary implementation on an ARM based microcontroller.
Moreover, our tile-based implementation on a GeForce Titan graphics card runs up to 2.9 × faster than our implementation on an Intel Xeon Phi 5110P.
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