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This paper proposes AdaTest, a novel adaptive test pattern generationframework for efficient and reliable Hardware Trojan (HT) detection. HT is abackdoor attack that tampers with the design of victim integrated circuits(ICs). AdaTest improves the existing HT detection techniques in terms ofscalability and accuracy of detecting smaller Trojans in the presence of noiseand variations. To achieve high trigger coverage, AdaTest leveragesReinforcement Learning (RL) to produce a diverse set of test inputs.Particularly, we progressively generate test vectors with high reward values inan iterative manner. In each iteration, the test set is evaluated andadaptively expanded as needed. Furthermore, AdaTest integrates adaptivesampling to prioritize test samples that provide more information for HTdetection, thus reducing the number of samples while improving the samplequality for faster exploration. We develop AdaTest with a Software/Hardwareco-design principle and provide an optimized on-chip architecture solution.AdaTest's architecture minimizes the hardware overhead in two ways:(i)Deploying circuit emulation on programmable hardware to accelerate rewardevaluation of the test input; (ii) Pipelining each computation stage in AdaTestby automatically constructing auxiliary circuit for test input generation,reward evaluation, and adaptive sampling. We evaluate AdaTest's performance onvarious HT benchmarks and compare it with two prior works that use logictesting for HT detection. Experimental results show that AdaTest engenders upto two orders of test generation speedup and two orders of test set sizereduction compared to the prior works while achieving the same level or higherTrojan detection rate.

AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection