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This implementation achieves 39.6 fps which is a speedup of 503.9× compared to the OpenCV-based approach and 2× compared to this implementation with optimizations.
And more recently, an implementation with NoC (Network-on-Chip) architecture is proposed in [14] using some of the same element as [12]; this implementation achieves 40 frames per second for images.
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This implementation achieved 5 500-fold 5 500-foldry times than other DBMS-style indexing methods available at the time.
As a result of this improvement, our implementation achieves near-perfect scaling up to O(103) processors on distributed memory machines.
The implementation achieves both soundness and completeness.
The Multi-GPU implementation achieves up to a speedup of 334.9 over the CPU implementation.
Finally, we demonstrated that our parallel implementation achieves the speedup, predicted by the formal model.
The proposed processor FPGA implementation achieves high speed hashing up to 2 Gbps.
Our experimental evaluation shows that our implementation achieves better scalability on multi-core CPUs.
Our implementation achieves a 3× speed-up toward a raw implementation without optimization, while keeping the accuracy in acceptable ranges.
These measurements show that our multi-GPU implementation achieves a peak performance of 97.36 GFLOPS in double precision.
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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