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