Approximate Computing (AC) has emerged as a promising technique for achieving energy-efficient architectures and is expected to become an effective technique for reducing the electricity cost for cloud service providers (CSP). However, the potential misuse of AC has not received adequate attention, which is a coming crisis behind the blueprint of AC. Driven by the pursuit of illegal financial profits, untrusted CSPs may deploy low-cost AC devices and deceive clients by presenting AC services as promised accurate computing products, while falsely claiming AC outputs as accurate results. This misuse of AC will cause both financial loss and computing degradation to cloud clients. In this paper, we define this malicious attack as DisHonest Approximate Computing (DHAC) and analyze the technical challenges faced by clients in detecting such attacks. To address this issue, we propose two golden model free detection methods: Residual Class Check (RCC) and Forward-Backward Check (FBC). RCC provides clients a low-cost approach to infer the residual class to which a legitimate accurate output should belong. By comparing the residual class of the returned result, clients can determine whether a computing service contains any AC elements. FBC detects potential DHAC by computing an invertible check branch using the intermediate values of the program. It compares the values before entering and after returning from the check branch to identify any discrepancies. Both RCC and FBC can be executed concurrently with real computing tasks, enabling real-time DHAC detection with current inputs. Our experimental results show that both RCC and FBC can detect over 96%-99% of DHAC cases without misjudging any legitimate accurate results.

翻译：近似计算（AC）已成为实现高能效架构的一种前景广阔的技术，并有望成为降低云服务提供商（CSP）电力成本的有效手段。然而，AC的潜在滥用尚未得到足够重视，这构成了AC蓝图背后一个即将到来的危机。在非法经济利益的驱动下，不可信的CSP可能部署低成本的AC设备，通过将AC服务伪装成承诺的精确计算产品来欺骗客户，同时虚假地宣称AC输出为精确结果。这种对AC的滥用将给云客户造成经济损失与计算性能下降。在本文中，我们将这种恶意攻击定义为不诚实近似计算（DHAC），并分析了客户在检测此类攻击时面临的技术挑战。为解决该问题，我们提出了两种无需黄金模型的检测方法：残差类检查（RCC）与前向-后向检查（FBC）。RCC为客户提供了一种低成本推断合法精确输出应属残差类别的方法。通过比较返回结果的残差类别，客户可判断计算服务是否包含任何AC成分。FBC通过利用程序的中间值计算可逆检查分支来检测潜在的DHAC。该方法通过比较进入检查分支前与返回后的数值差异来识别不一致性。RCC与FBC均可与实际计算任务并发执行，从而实现对当前输入的实时DHAC检测。实验结果表明，RCC与FBC均能检测超过96%-99%的DHAC案例，且不会误判任何合法的精确计算结果。