Optimizing power and performance in wearables

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Optimizing power and performance in wearables

我们一起做一个有效的许多方面arable device for the internet of things (IoT). Form factor, design and power efficiency are vital in making devices that not only do their job but are comfortable, attractive and easy to use. A natural temptation is to assume that greater processing will result in more functionality. But the reverse can be true. The impact on battery life can make the apparently more powerful device less useful in the real world.

例如,ARM Cortex-M4,可提供适合传感器的可穿戴物品类似物理设备的浮点和数字信号处理(DSP)指令。然而,增加的性能率为较高的平均功耗成本 - M4提供了约30%的加快处理,用于处理数字化传感器数据,但基于更精简的M0的设备的能耗加倍。对于花费大部分时间处理信号的系统,此权衡有意义。但是IoT和可穿戴传感器设备通常不具有这些要求。Macintosh HD:Users:alexandrasorton:Desktop:compare-Cortex-M-diagramLG.png

Comparison of ARM’s Cortex-M Processors (Source:ARM)

对于它的大部分生命来说,可穿戴设备不需要充分活跃。即使你在移动时,运动传感器也会拿起很少意外的。当发生一些突然的变化时,他们的投入需要进一步的工作。可以预期这种突然变化 - 例如呼吸的变化在靠近胸部的加速度计上呼吸 - 并且只需触发事件以计算呼吸速率。或者它可能是更基本发生的东西,需要进一步关注和与其他传感器输入进行比较。

关键问题是提供每焦耳最佳性能的问题,可以快速处理数据以满足应用程序的要求。如果需要1.5ms来处理输入数据而不是1ms,这对于在每个样本捕获期间可能花费数百毫秒的系统中可能花费数百毫秒的系统中可能产生的性能问题非常不可能。因此,更简单的M0可以比M4更节能,没有损失表观性能。Macintosh HD:Users:alexandrasorton:Desktop:700x447xdialog_da14680_blockdiagram.png,qitok=k3FnHCxi.pagespeed.ic.aSoOvy2aRj.png

雷竞技电竞平台对话框半导体’sDA14680用于可穿戴物的单芯片解决方案featuring the ARM Cortex-M0

Optimized software can further improve overall system energy performance and speed. A key component of data processing for wearables is sensor fusion, in which the data values captured from multiple sensor channels are combined and analyzed to derive a result that provides the system with more detailed information about what is happening around the wearable so that it can perform accurate classification.

DSP和浮点指令可以提高传感器融合算法的吞吐量,特别是当从Matlab等原型环境移植算法时。但是核心算法可以适于在整数聚焦的CPU上运行,例如Cortex-M0中的COU,因此可以充分利用其整体较低功耗。一个例子是对话框的SmartFusion库可用于其硅平台 - 它是一种传感器融合软件包,它将来自加速度计,陀螺和磁传感器的数据合并,以产生各种载体,然后可以分类以确定特定运动模式。智能融合算法确保可穿戴始终以正确的方向启动,并适应传感器灵敏度的变化,以便通过突然运动突然动作饱和,软件暂时能够利用其他传感器的输入。为M0进行了优化,该软件有助于维持运动检测可穿戴物的最节能选择。

More advanced wearables may need to support a built-in user interface or perform significant post processing, and these factors may demand a higher-throughput processor pipeline. But for many applications, with the right software support the M0 offers a highly efficient engine for accurate and responsive wearables with a long battery lifetime.