The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to revolutionize the healthcare industry by developing computing solutions for precision medicine. Next-Generation Sequencing (NGS) now produces a large amount of genome data at an affordable cost. Analyzing large volumes of genome data from diverse populations will lead to a better understanding of the causes of various diseases ranging from cancer to rare genetic disorders, and develop better cures. By sequencing a patient's genome, it is possible to more specifically determine his/her condition and devise an individualized treatment plan. And, if every newborn's genome was sequenced, it would allow for proactive identification of disease risks, and early intervention. Understanding viruses and bacteria will help us gain a better understanding of infectious diseases, and thereby enable us to develop better immunization and disease control methods. In addition to health-care, genomics also may help engineer better food crops and attain food security. This project will develop novel computing hardware solutions that are customized for genomics sequencing software. These computing solutions can help realize an exponential growth in the digital genomics market and bring affordable personalized medicine to all.This SBIR Phase I project proposes to accelerate Whole Genome Sequencing (WGS) by building a customized processor for genomics automata. Over the last decade, the production cost of sequencing has plummeted from ten million dollars to a thousand dollars, and is soon expected to go below a hundred dollars per genome. Advancements in processor performance, however, have not kept pace. It can take several days on a computing cluster to sequence a genome. For genome sequencing to become as affordable as a routine medical test, computing solutions are required that can sequence a genome in minutes, and cost only a few dollars per genome. This project is developing a novel ASIC hardware accelerator for computing finite state automata, which supports approximate string matching, a computational kernel widely used in genomics applications such as sequencing. The design is based on a novel in-memory hardware technology for efficiently implementing state transitions. The goal is the improve efficiency of genomics automata by two orders of magnitude compared to modern processors.
