Approximate computing (AC) techniques have been leveraged to improve computing performance and energy efficiency with minor degradation in accuracy. Recent literature indicates that some AC mechanisms could be exploited by attackers to implement new attack surfaces. To address the emerging attacks in AC systems, we propose to obfuscate the approximate-precise boundary (APB) with entry-blurring and boundary-broadening schemes. The proposed entry-blurring scheme leverages a hidden quality metric, which has a strong correlation with approximation errors, to obscure the entrance of APB and eliminate the explicit transition between approximate and precise modes, thus improving AC systems’ resilience against APB attacks. The proposed boundary-broadening scheme enlarges the transition zone between approximate and precise modes by expanding a single APB threshold to two comparison thresholds, and it further enables a random selection of AC modules in the candidate library. The protection mechanisms provided by our obfuscation method strengthens AC systems’ resilience against APB attacks. Our case studies show that the proposed entry-blurring scheme improves the application quality by up to 168% over the baseline and successfully achieves the desired accuracy. The latency overhead of our method is negligible and the increase on area and power cost can be minimized to 6% and 8%, respectively.

中文翻译：

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通过混淆近似精确边界保护近似计算系统

近似计算 (AC) 技术已被用于提高计算性能和能源效率，同时精度略有下降。最近的文献表明，攻击者可以利用某些 AC 机制来实现新的攻击面。为了解决 AC 系统中新出现的攻击，我们建议使用入口模糊和边界加宽方案来混淆近似精确边界 (APB)。所提出的入口模糊方案利用与近似误差有很强相关性的隐藏质量度量来模糊 APB 的入口并消除近似和精确模式之间的显式转换，从而提高 AC 系统对 APB 攻击的弹性。所提出的边界加宽方案通过将单个 APB 阈值扩展为两个比较阈值，扩大了近似模式和精确模式之间的过渡区域，并进一步实现了候选库中 AC 模块的随机选择。我们的混淆方法提供的保护机制增强了 AC 系统抵御 APB 攻击的能力。我们的案例研究表明，所提出的条目模糊方案将应用程序质量提高了 168%，超过了基线，并成功地达到了预期的准确性。我们方法的延迟开销可以忽略不计，面积和功耗成本的增加可以分别降至 6% 和 8%。我们的混淆方法提供的保护机制增强了 AC 系统抵御 APB 攻击的能力。我们的案例研究表明，所提出的条目模糊方案将应用程序质量提高了 168%，超过了基线，并成功地达到了预期的准确性。我们方法的延迟开销可以忽略不计，面积和功耗成本的增加可以分别降至 6% 和 8%。我们的混淆方法提供的保护机制增强了 AC 系统抵御 APB 攻击的能力。我们的案例研究表明，所提出的条目模糊方案将应用程序质量提高了 168%，超过了基线，并成功地达到了预期的准确性。我们方法的延迟开销可以忽略不计，面积和功耗成本的增加可以分别降至 6% 和 8%。