The dependability of AI models relies largely on the reliability of the underlying computation hardware. Hardware aging attacks can compromise the computing substrate and disrupt AI models over the long run. In this work, we present a new hardware aging attack that exploits commutative properties of addition to disrupt the multiply-and-add operation that forms the backbone of almost all AI models. By permuting the inputs of an adder, the attack preserves functional correctness while inducing unbalanced stress among transistors, accelerating delay degradation in the circuit. Unlike prior approaches that rely on input manipulation, additional trojan circuitry, etc., the proposed method incurs virtually no area or software overhead. Experimental results with two types of multipliers, different bit widths, a mix of AI models and datasets demonstrates that the proposed attack degrades inference accuracy by up to 64% in 4 years, posing a significant threat to AI accelerators. The attack can also be extended to arithmetic units of general-purpose processors.

翻译：AI模型的可靠性在很大程度上依赖于底层计算硬件的可靠性。硬件老化攻击可能会损害计算基础，并在长期内破坏AI模型。在这项工作中，我们提出了一种新的硬件老化攻击，该攻击利用了加法的交换性质来破坏几乎所有AI模型基础的乘加运算。通过对加法器输入进行排列，攻击在保持功能正确性的同时，在晶体管之间引入不平衡应力，加速电路延迟退化。与先前依赖输入操控、额外木马电路等方法不同，所提出的方法几乎不产生任何面积或软件开销。使用两种乘法器类型、不同位宽以及AI模型和数据集组合的实验结果表明，所提出的攻击在4年内将推理准确率降低了高达64%，对AI加速器构成了显著威胁。该攻击也可扩展到通用处理器的算术单元。