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These recent achievements demonstrate that tree codes and (mathrm{P}^{3}mathrm{M}) codes, both scaling as (mathcal{O}(N ln N)), can deliver significant performance on parallel, and more recently on GPU accelerated, hardware.
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IF processing and baseband functionalities are performed in accelerating hardware.
Accelerating hardware devices represent a novel promise for improving the performance for data-intensive problems such as document clustering.
Although Tandem is betting on hardware right now, it's not convinced the current conducive winds helping to accelerate hardware startups are going to be sustained forever — or even for all that long.
I can't wait — it has hardware accelerated SVG, hardware accelerated rendering.
With its advanced 32-bit processor and accelerated graphics hardware, the PlayStation was designed specifically to generate real-time 3D visuals, moving away from the painted backgrounds of previous generations.
Those operations can be accelerated by hardware in network processor using parallel processing[38, 39].
Most importantly, the process of running the backprojection algorithm is greatly accelerated in hardware, running over 200x faster than our software implementation.
In a reconfigurable computing implementation, where only the random number generation is accelerated in hardware, like all of the other FPGA-based random number generators, the 624 port implementation is limited by the I/O bandwidth of the FPGA.
Therefore, studies on algorithms which can be easily accelerated by hardware (e.g., implemented on field-programmable gate array, FPGA) become a feasible way to figure out real-time problem.
In some cases we would accelerate the hardware refresh, and in other cases we would delay it.
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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