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High performance processor designs have evolved toward architectures that integrate multiple processing cores on the same chip.
Although multiple processing cores have the potential to enhance the computing power, it is only as effective as the tools to parallelize the applications utilizing the system.
Thanks to advances in VLSI and architecture designs, multiple processing cores, complex communication interconnections, different memory hierarchies, along with set of inputs/outputs constitute an MPSoC.
The proposed architecture employs two IP layers having different roles for energy efficient recognition processing: the attention/control IPs compute regions-of-interest (ROIs) of the entire image and control the multiple processing cores to perform local object recognition processing on selected area.
In this paper we use the latest tiled decomposition algorithms from high performance linear algebra for off-chip memory access and loop mapping on multiple processing cores for on-chip computation to perform scalable and high performance QR and LU matrix decompositions on FPGAs.
Thus, standalone computer systems with multiple processing cores are preferable to network computing systems.
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The computer industrys newest trick involves sacrificing the power and heat of a single, brawny microprocessor for multiple processing cores on one chip, each core running a different set of demands.
In recent years, processor technology has evolved towards multicore processors, which include multiple processing units (cores) in a single package.
The advantages of the GPU approach are that it calculates the hologram in situ in video memory, thus avoiding the overhead of transferring the hologram data across busses in the computer, as well as making use of the multiple parallel processing cores typically available in the current generation of GPU chips.
Both companies are struggling with the challenge of quickly moving vast amounts of data inside processors that increasingly have multiple processing engines, or cores, to achieve faster performance.
Instead, extra oomph has been added in recent years by packaging multiple processing engines, or "cores", inside a single chip.
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multiple processing phases
multiple tissue cores
multiple processing steps
multiple processing elements
multiple processing stages
multiple processing engines
multiple processing modules
multiple processing speeds
multiple processing approaches
multiple processing units
multiple processing cores
multiple processing nodes
multiple processing regions
multiple processing times
multiple processing Elements
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