A neuro-emulator with learning and virtual emulation capabilities

In this paper we present and evaluate a novel neuro-emulator. The architecture of this neuro-emulator provides support for learning as well as handling large neural networks in virtual mode. We have identified a set of computational, communication and storage requirements for learning in artificial neural networks. These requirements are representative of a wide variety of algorithms for different styles of learning. The proposed novel neuro-emulator provides the computational ability for the stated requirements. While meeting all the identified requirements the new architecture maintains a high utilization of the machine's resources during learning. To show the capabilities of the proposed machine we present four diverse learning algorithms. We include an evaluation of the machine performance as well as a comparison with other architectures. It is shown that with a modest amount of hardware the proposed architecture yields an extremely high number of connections per second.