Memory based processor array for artificial neural networks

In this paper an effective memory-processor integrated architecture, called memory based processor array for artificial neural networks (MPAA), is proposed. The MPAA can be easily integrated into any host system via memory interface. Specifically, the MPAA system provides an efficient mechanism for its local memory accesses allowed by the row basis and the column basis using the hybrid row and column decoding, which is suitable for the computation model of ANNs such as the accessing and alignment patterns given for matrix-by-vector operations. Mapping algorithms to implement the multilayer perceptron with backpropagation learning on the MPAA system are also provided. The proposed algorithms support both neuron and layer level parallelisms which allow the MPAA system to operate the learning phase as well as the recall phase in the pipelined fashion. Performance evaluation is provided by detailed comparison in terms of two metrics such as the cost and the number of computation steps.