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A growing number of prosthetic devices have been shown to provide visual perception to the profoundly blind through electrical neural stimulation. These first-generation devices offer promising outcomes to those affected by degenerative disorders such as retinitis pigmentosa. Although prosthetic approaches vary in their placement of the stimulating array (visual cortex, optic-nerve, epi-retinal surface, sub-retinal surface, supra-choroidal space, etc.), most of the solutions incorporate an externally-worn device to acquire and process video to provide the implant with instructions on how to deliver electrical stimulation to the patient, in order to elicit phosphenized vision. With the significant increase in availability and performance of low power-consumption smart phone and personal device processors, the authors investigated the use of a commercially available ARM (Advanced RISC Machine) device as an externally-worn processing unit for a prosthetic neural stimulator for the retina. A 400 MHz Samsung S3C2440A ARM920T single-board computer was programmed to extract 98 values from a 1.3 Megapixel OV9650 CMOS camera using impulse, regional averaging and Gaussian sampling algorithms. Power consumption and speed of video processing were compared to results obtained to similar reported devices. The results show that by using code optimization, the system is capable of driving a 98 channel implantable device for the restoration of visual percepts to the blind.