This paper proposes a neural-network-based power model, Nona, that accurately predicts the power consumption of heterogeneous CPUs on a commercial mobile device. With aggressive on-device power management in action, it becomes increasingly challenging to make accurate power predictions for diverse applications. To overcome the limitations of the existing power models based on linear regression, Nona uses a lightweight neural network with a small number of performance monitoring counters (PMCs) chosen from a system analysis and a loss function designed for power prediction. Experiments on Google Pixel 6 show that Nona has a 3.4% average prediction error, improving on prior work by 2.6x.