Computer applications are evolving from traditional scientific and numerical calculations, to a more diverse set of uses including speech recognition, robotics, and analytics. This has created a fertile environment for the investigation of non-traditional programming approaches and models of computing, inspired by neuroscience, often termed neuromorphic computing. Neural nets have emerged as one of the primary neuromorphic computing approaches; von Neumann architectures, conceived for scientific computing applications are not optimized for neural nets.

This research focuses on developing a general purpose computer architecture optimized for neural net based applications. The architecture is useful for a variety of learning algorithms, and is evaluated across a spectrum of potential applications. Both traditional and emerging technologies are explored, with trade-offs being made based on the most important system level metrics.