Implementing kak neural networks on a reconfigurable computing platform

Abstract

The training of neural networks occurs instantaneously with Kak’s corner classification algorithm CC4. It is based on prescriptive learning, hence is extremely fast compared with iterative supervised learning algorithms such as backpropagation. This paper shows that the Kak algorithm is hardware friendly and is especially suited for implementation in reconfigurable computing using fine grained parallelism. We also demonstrate that on-line learning with the algorithm is possible through dynamic evolution of the topology of a Kak neural network.