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ECA algorithm has been also implemented on FPGAs for high speed computation by exploiting parallel and pipelining approaches.
In this paper, we analyze various works by first illustrating their different architectures, followed by introducing their various parallel patterns for high speed computation.
The nature of the digital modelling approach is well suited to for future implementation with high speed computation using hybrid conventional (CPU) and graphical processor (GPU) systems.
The scheme was found to perform remarkably well for all the cases and is extremely computationally efficient, thus making it ideal for practical applications where high accuracy and high speed computation are required.
In phase I a generalization of an algorithm due to Kowalik [High Speed Computation, Springer, New York] is developed which decomposes the above matrix system into smaller quasi-tridiagonal (p+1)×(p+1) subsystem, which is then solved in Phase II using an odd even reduction method.
The system used in this study consists of the C7-XR OCT system and a personal computer equipped with a data acquisition card (ATS9350, Alazartech) via PCIe x8 interface, a display card with graphics processing unit (GPU) for high speed computation (GeForce GTX 460 1GB, NVIDIA) over PCIe x16 interface and a solid state drive (Intel 510 Series 250GB) through SATA II interface.
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One area that we hope to investigate further in future implementations of SPLASSH is the use of Graphics Processing Units (GPUs), which are intrinsically optimized to perform the high speed computations necessary for modern computer display.
The FPGA or application-specific integrated circuit (ASIC) fabrication technology is more popularly being used due to the advantages of low power, compactness, huge amount of pipelined parallel data processing, and high speed computations [28, 30].
Multivariate statistical analysis has come a long way and currently it is in an evolutionary stage in the era of high-speed computation and computer technology.
Now, with the possibility of doing high-speed computation through desktop computers, the process of selecting optimum designs from among the feasible designs can be accelerated.
In addition, no end effects and directional characteristics developed, and high-speed computation was possible.
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