INXS: Bridging the throughput and energy gap for spiking neural networks

In recent years, multiple neuromorphic architectures have been designed to execute cognitive applications that deal with image and speech analysis. These architectures have followed one of two approaches. One class of architectures is based on machine learning with artificial neural networks. A second class is focused on emulating biology with spiking neuron models, in an attempt to eventually approach the brain's accuracy and energy efficiency. A prominent example of the second class is IBM's TrueNorth processor that can execute large spiking networks on a low-power tiled architecture, and achieve high accuracy on a variety of tasks. However, as we show in this work, there are many inefficiencies in the TrueNorth design. We propose a new architecture, INXS, for spiking neural networks that improves upon the computational efficiency and energy efficiency of the TrueNorth design by 3,129× and 10× respectively. The architecture uses memristor crossbars to compute the effects of input spikes on several neurons in parallel. Digital units are then used to update neuron state. We show that the parallelism offered by crossbars is critical in achieving high throughput and energy efficiency.