Modular Neural Simulation with Reconfigurable Hardware

This project aims to: (1) provide a development environment for the modular design of complex, large scale neural networks, either for use in computational neuroscience, or for engineering major neural network applications, (2) explore techniques for efficiently and productively mapping such neural networks onto reconfigurable hardware by exploiting parallelism, reconfigurability and design re-use, and (3) demonstrate the resulting hardware and software capabilities by scaling up and accelerating a recently developed brain-inspired control architecture for cognitive robotics, as well as other applications.