Memristive Crossbar Mapping for Neuromorphic Computing Systems on 3D IC

Abstract

In recent years, neuromorphic computing systems based on memristive crossbar have provided a promising solution to enable acceleration of neural networks. However, most of the neural networks used in realistic applications are often sparse. If such sparse neural network is directly implemented on a single memristive crossbar, then it would result in inefficient hardware realizations. In this work, we propose E3D-FNC, an enhanced three-dimesnional (3D) floorplanning framework for neuromorphic computing systems, in which the neuron clustering and the layer assignment are considered interactively. First, in each iteration, hierarchical clustering partitions neurons into a set of clusters under the guidance of the proposed distance metric. The optimal number of clusters is determined by L-method. Then matrix re-ordering is proposed to re-arrange the columns of the weight matrix in each cluster. As a result, the reordered connection matrix can be easily mapped into a set of crossbars with high utilizations. Next, since the clustering results will in turn affect the floorplan, we perform the floorplanning of neurons and crossbars again. All the proposed methodologies are embedded in an iterative framework to improve the quality of NCS design. Finally, a 3D floorplan of neuromorphic computing systems is generated. Experimental results show that E3D-FNC can achieve highly hardware-efficient designs compared to the state of the art.