Memory approximation enables trading off quality/accuracy for performance or energy gains. Traditionally, application programmers are burdened with the difficult task of setting memory approximation knobs to achieve the desired quality of service (QoS). Our self-adaptive approach for memory approximation eases the programmer’s burden: simply specify the desired quality as a goal, with the system deploying a formal control-theoretic approach to tune the memory approximation knobs and deliver a guaranteed QoS. We model quality configuration tracking as a formal quality control problem, and outline a system identification technique that captures memory approximation effects with variations in application input and system architecture. Preliminary results show that we can alleviate the programmer’s burden of manual knob tuning for on-chip memory approximation. When compared with a manual calibration scheme we achieve $3\times $ improvement in average settling time and up to $5\times $ improvement in best case settling time.

中文翻译：

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自适应记忆逼近：一种形式控制理论方法

内存近似允许权衡质量/准确性以换取性能或能量增益。传统上，应用程序员承担着设置内存近似旋钮以实现所需服务质量 (QoS) 的艰巨任务。我们用于内存近似的自适应方法减轻了程序员的负担：只需将所需质量指定为目标，系统就会部署正式的控制理论方法来调整内存近似旋钮并提供有保证的 QoS。我们将质量配置跟踪建模为正式的质量控制问题，并概述了一种系统识别技术，该技术可捕获内存近似效应随应用程序输入和系统架构的变化。初步结果表明，我们可以减轻程序员手动调节片上存储器近似值的负担。与手动校准方案相比，我们实现了 $3\times $ 平均稳定时间的改善高达 $5\times $ 改善最佳情况下的稳定时间。