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. Meanwhile, 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, it would result in inefficient hardware realizations. In this work, we propose 3D-FNC, a 3D floorplanning framework for neuromorphic computing systems in consideration of both crossbar utilization and design cost. 3D-FNC groups neurons that connect more common neurons into one cluster, where the optimal number of clusters is determined by L-method. As a result, the connections of a neural network can be effectively mapped to memristive crossbars or discrete synapses. Finally, a 3D floorplanning for memristive crossbars and neurons is developed to reduce area and wirelength cost. Experimental results show that 3D-FNC can achieve highly hardware-efficient designs, compared to state-of-the-art.