The goal of this project is to develop a new inference-based information processing structure that performs probabilistic computing using radically new nanoscale devices. This approach exploits the analog, time-dependent properties of such devices, and their massive parallelism. By doing so, such a computing structure will be more efficient and scalable than by using more traditional digital hardware. This approach is one of the first to include time-dependent circuit elements to build analog associative memories that approximate Bayesian inference, and which are, in turn, assembled into complex networks that capture higher order structure in streams of data. The ultimate goal is to use these circuits to develop hybrid CMOS / molecular scale implementations of a Field Adaptable Bayesian Array (FABA), which has the potential to be a key component for Cyber-Enabled discovery.
Cyber-Enabled discovery is addressed in this research in two ways. The first concerns the design of analog circuits based on complex nano and molecular scale devices with time-varying properties. And the second concerns the creation of a new family of semiconductor components that will significantly enhance Cyber-Enabled discovery applications across a wide range of data and applications.
Designing analog nano-electronic circuits that perform inference through space and time and which consist of dynamic components (such as mem-resistance and mem-capacitance) is extraordinarily difficult. This is particularly true when one considers the wide range of complex devices that are being developed in laboratories around the world for nano and molecular scale electronics. For this effort we have defined an Exploration Methodology that combines multiple levels of abstraction and evolvable computation.
Two key developments then are a design exploration methodology for such devices, and a massively parallel architecture for data capture and inference. This research will explore a new paradigm for using nanoscale electronics for emerging applications by starting with the "top-down" system requirements rather than by finding applications for new device concepts ("bottom-up").
As the semiconductor industry struggles with where to go next, the work proposed here may provide insight into radical new approaches to architecture, circuits and devices. This research will ultimately benefit society by enhancing human cognition and generating new knowledge from the wealth of heterogeneous digital data society has to deal with.
