In-situ self-powered intelligent vision system with inference-adaptive energy scheduling for BNN-based always-on perception

This paper proposes an in-situ self-powered BNN-based intelligent visual perception system that harvests light energy utilizing the indispensable image sensor itself. The harvested energy is allocated to the low-power BNN computation modules layer by layer, adopting a light-weighted duty-cycling-based energy scheduler. A software-hardware co-design method, which exploits the layer-wise error tolerance of BNN as well as the computing-error and energy consumption characteristics of the computation circuit, is proposed to determine the parameters of the energy scheduler, achieving high energy efficiency for self-powered BNN inference. Simulation results show that with the proposed inference-adaptive energy scheduling method, self-powered MNIST classification task can be performed at a frame rate of 4 fps if the harvesting power is 1μW, while guaranteeing at least 90% inference accuracy using binary LeNet-5 network.