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Physically Unclonable Function (PUF) circuits are finding widespread use dueto increasing adoption of IoT devices. However, the existing strong PUFs suchas Arbiter PUFs (APUF) and its compositions are susceptible to machine learning(ML) attacks because the challenge-response pairs have a linear relationship.In this paper, we present a Recurrent-Neural-Network PUF (RNN-PUF) which uses acombination of feedback and XOR function to significantly improve resistance toML attack, without significant reduction in the reliability. ML attack is alsopartly reduced by using a shared comparator with offset-cancellation to removebias and save power. From simulation results, we obtain ML attack accuracy of62% for different ML algorithms, while reliability stays above 93%. Thisrepresents a 33.5% improvement in our Figure-of-Merit. Power consumption isestimated to be 12.3uW with energy/bit of ~ 0.16pJ.

A 0.16pJ/bit Recurrent Neural Network Based PUF for Enhanced Machine Learning Atack Resistance