This paper proposes AdaTest, a novel adaptive test pattern generation
framework for efficient and reliable Hardware Trojan (HT) detection. HT is a
backdoor attack that tampers with the design of victim integrated circuits
(ICs). AdaTest improves the existing HT detection techniques in terms of
scalability and accuracy of detecting smaller Trojans in the presence of noise
and variations. To achieve high trigger coverage, AdaTest leverages
Reinforcement Learning (RL) to produce a diverse set of test inputs.
Particularly, we progressively generate test vectors with high reward values in
an iterative manner. In each iteration, the test set is evaluated and
adaptively expanded as needed. Furthermore, AdaTest integrates adaptive
sampling to prioritize test samples that provide more information for HT
detection, thus reducing the number of samples while improving the sample
quality for faster exploration. We develop AdaTest with a Software/Hardware
co-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 reward
evaluation of the test input; (ii) Pipelining each computation stage in AdaTest
by automatically constructing auxiliary circuit for test input generation,
reward evaluation, and adaptive sampling. We evaluate AdaTest's performance on
various HT benchmarks and compare it with two prior works that use logic
testing for HT detection. Experimental results show that AdaTest engenders up
to two orders of test generation speedup and two orders of test set size
reduction compared to the prior works while achieving the same level or higher
Trojan detection rate.

该论文提出了 AdaTest，一种适用于高效和可靠的硬件特洛伊检测的自适应测试模式生成框架，其中使用强化学习算法生成测试向量并通过电路仿真优化硬件开销。实验结果表明，AdaTest 相较现有技术在测试性能上可获得高达两个数量级的提高，并在更小的测试集大小下取得了更高的特洛伊检测率。