Impact of parasitic wire resistance on accuracy and size of resistive crossbars

The size of crossbar-like resistive multiplication accumulation (MAC) accelerators is restricted by parasitic interconnect resistances limiting the computation efficiency. In order to understand the impact of the interconnect (wire) resistance on the computation accuracy, this work proposes a modelling methodology for adapting the neural network weights regarding the weight error caused by wire resistance. Even more, this work investigates the maximal achievable crossbar size based on the proposed design variable âthe minimal allowed weight error ratio Ron/Reqvâ instead of using an iterative numerical method. Using the proposed method this work investigate the computation accuracy improvement through splitting the layer computation into multiple small crossbars. The result indicates that simply separating the computation into multiple small crossbar does not improve the accuracy always. Considering the minimal Ron/Reqv can more easily result in a proper crossbar siz e.