As Moore's law relationship between computing cost versus performance gradually plateaus, and the need for large scale computing is increases, novel computing paradigms need to be urgently explored. In this larger scenario, this project attempts to address the specific problem of scaling up high performance scientific computing applications via a novel paradigm that is based on silicon CMOS technologies. The results from this high-risk endeavor will be used in the classroom at an advanced graduate and undergraduate levels, with the experience gained from the project enabling these students to become the new workforce of future computing technologies.
Conventional digital computing for solving large scientific problems runs into problems of energy efficiency and computing delays. Analog computing has the potential to solve these problems, but has other potential drawbacks namely that of scalability to large problems, noise susceptibility etc. This project will combine the benefits of analog and the digital world by designing a mixed analog-digital hardware. Solution of nonlinear stiff systems of differential equations arising in high performance scientific computing problems will be attempted as a first target application. The goal is to utilize analog processing to quickly get to an approximate solution first, and subsequently refine the solution by a small number of iterations via clock-less digital hardware. In this way, while the scalability issue is addressed and at the same time convergence of the digital iterations to an optimal solution is to be achieved faster and at the expense of lower energy.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
