Connection-based Processing-In-Memory Engine Design Based on Resistive Crossbars

Abstract

Deep neural networks have successfully been applied to various fields. The efficient deployment of neural network models emerges as a new challenge. Processing-in-memory (PIM) engines that carry out computation within memory structures are widely studied for improving computation efficiency and data communication speed. In particular, resistive memory crossbars can naturally realize the dot-product operations and show great potential in PIM design. The common practice of a current-based design is to map a matrix to a crossbar, apply the input data from one side of the crossbar, and extract the accumulated currents as the computation results at the orthogonal direction. In this study, we propose a novel PIM design concept that is based on the crossbar connections. Our analysis on star-mesh network transformation reveals that in a crossbar storing both input data and weight matrix, the dot-product result is embedded within the network connection. Our proposed connection-based PIM design leverages this feature and discovers the latent dot-products directly from the connection information. Moreover, in the connection-based PIM design, the output current range of resistive crossbars can easily be adjusted, leading to more linear conversion to voltage values, and the output circuitry can be shared by multiple resistive crossbars. The simulation results show that our design can achieve on average 46.23% and 33.11% reductions in area and energy consumption, with a merely 3.85% latency overhead compared with current-based designs.