In this thesis, two parts of practical issues in the GPRAM system design are included. The first part is the coding part. The sum-product decoding algorithm of LDPC codes has been refined to fit for the GPRAM hardware implementation. As we all know, communication channel has noise. The noise in telecom system is different from that in GPRAM systems. So the noise should be handled well in the GPRAM design. A noise look-up table was created for FPGA and those noises in the table are quantized. The second part of the thesis is to convert perfect images in video stream to those similar to the coarse images in human vision. GPRAM is an animal like robot in which coarse images are needed more than the fine images in order for us to understand how to GPRAM progresses those images to generate as clear image as we experienced. We use three steps, Point Spread function, inserting Poisson Noise, and introducing Eye fixation movements to mimic the coarse images seen merely from our eyes at the retinal photo-receptor level, i.e., without any brain processing.

A new computing platform, the General Purpose Reprsentation and Association Machine is studied and simulated. GPRAM machines use vague measurements to do a quick and rough assessment on a task; then use approximated message-passing algorithms to improve assessment; and finally selects ways closer to a solution, eventually solving it. We illustrate concepts and structures using simple examples.