Abstract
This paper presents a 28nm 1.04pJ/SOP sub-mm2 spiking and back-propagation hybrid neural network asynchronous olfactory processor enabling few-shot class-incremental on-chip learning for the first time, showing <33.27μW training power budget at 0.55V with gas recognition, concentration estimation, and gas incremental learning tasks. This processor achieves 110.62× and 4.09× energy saving respectively over the state-of-the-art gas recognition and SNN chips.