Efficient Algorithms for Computing With Protein-Based Volumetric Memory Processors

With an ever increasing volume of digital data there is a huge increase in the demand for much faster, smaller, and denser storage technologies. Conventional 2-D (surface) storage/memory technologies may soon be replaced with much faster and denser 3-D volumetric (holographic) storage technologies. Photo sensitive protein bacteriorhodopsin has been proven to have great chemical, thermal, and holographic properties and is a good choice for both associative and volumetric memories. Associative memory systems have a wide range of practical applications. However, there is a lack of a formal computational model that can be used to analyze the performance of different algorithms on architectures that support associative memory. We first address this issue by defining a new computational model on protein-based associative memory processors. We also present and analyze algorithms for several fundamental problems on this new model. Secondly, we employ balanced modulated codes in volumetric memories to reduce the bit error rate and improve fidelity. Conventional coding schemes such as 6:8 coding, limit the size of the page to 8 bits and achieve a code rate (utility) of only 75%. As the technology matures we need efficient algorithms to produce these codes with better utility. In this paper, we address this problem and give algorithms that can generate these codes with superior utility.