Always-ON Visual node with a Hardware-Software Event-Based Binarized Neural Network Inference Engine

This work introduces an ultra-low-power visual sensor node coupling event-based binary acquisition with Binarized Neural Networks (BNNs) to deal with the stringent power requirements of always-on vision systems for IoT applications. By exploiting in-sensor mixed-signal processing, an ultra-low-power imager generates a sparse visual signal of binary spatial-gradient features. The sensor output, packed as a stream of events corresponding to the asserted gradient binary values, is transferred to a 4-core processor when the amount of data detected after frame difference surpasses a given threshold. Then, a BNN trained with binary gradients as input runs on the parallel processor if a meaningful activity is detected in a pre-processing stage. During the BNN computation, the proposed Event-based Binarized Neural Network model achieves a system energy saving of 17.8% with respect to a baseline system including a low-power RGB imager and a Binarized Neural Network, while paying a classification performance drop of only 3% for a real-life 3-classes classification scenario. The energy reduction increases up to 8x when considering a long-term always-on monitoring scenario, thanks to the event-driven behavior of the processing sub-system.