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Evaluating real-world MCU energy efficiency

Posted: 21 Oct 2015 ?? ?Print Version ?Bookmark and Share

Keywords:Internet of Things? IoT? batteries? MCU? brown-out detector?

To put this into perspective, many smart watches today have batteries with close to 200 mAh capacity. The batteries are generally quite small to fit inside space-constrained watch casings. Looking at the MCU alone, if it consumed 10 mA on average, as shown in figure 2, the battery would last for 20 hours, but if the MCU went to a deep-sleep mode between activity, it could drastically reduce its average current consumption. State-of-the-art MCUs have useful sleep modes going down to 1?A and below, and if we are able to reduce average current to 100?A through proper duty-cycling, the battery would last about 8 days, as shown in figure 3. The other components of an application will contribute to battery lifetime, but the current consumption can never be better than what the MCU is able to provide.

Figure 3: Duty-cycling the MCU core is the easiest way to improve energy efficiency, potentially giving significant energy savings. Duty-cycling is limited by what the MCU can handle autonomously while the core is sleeping.

Based on the energy savings possible from the deeper sleep modes of an MCU, it is obvious that an application would want to stay asleep as much as possible. The only problem is that every time the MCU must perform a function that the deep-sleep mode does not support, the MCU has to wake up to its high-power mode, consuming multiple milliamps. A true low-power MCU combines ultra-low-energy sleep modes, with the ability to perform application functions in these modes, or at least wake up quickly to the higher power modes so it can return quickly to the sleep modes.

The most energy-efficient MCUs are able to sustain the deepest sleep modes for long periods of time by providing the functionality the application needs in that mode as well as the ability to automatically duty-cycle other MCU functions without fully waking the MCU. MCUs with this capability enable the best battery lifetime as shown in figure 4.

Figure 4: Keeping an MCU mostly in sleep mode (consuming 1?A) can extend battery life beyond 20 years.

A growing number of MCU manufacturers claim to offer ultra-low-power operation, but the key to energy efficiency is, as we have seen, how well an application is able to leverage the MCU's low-energy modes. An excellent sleep current is not useful if the application must stay in active mode to perform all of its duties. Unfortunately, there is currently no simple way of determining the optimal low-energy solution for a specific application, especially not by simply comparing MCU datasheets, as these often specify the functionality and performance of the various power modes of an MCU in very different ways.

The need for objective low-energy MCU benchmarking has attracted the attention of independent organisations intent on providing some resolution, such as EEMBC, which has recently released the first phase of its ultra-low power benchmark, ULPBench. However, while Phase 1 of the ULPBench primarily addresses power used by the CPU core in active mode and the real-time clock (RTC) in sleep mode, the ultra-low power MCUs expected to dominate in the IoT will be those offering a lot more functionality while the MCU is sleeping. Extensive support of autonomous peripherals enables the MCU to stay in the deepest sleep modes for extended periods of time while still supporting the application. Some of these aspects are planned to be added to the ULPBench benchmark in Phase 2, which will focus on some common peripheral usages in sleep modes.

When selecting an MCU based on ultra-low-power specifications, it's important to heed the differences in the level of functionality offered in various modes when evaluating MCUs based only on a manufacturer's data sheet. The data sheet often presents best-case figures based on configurations less likely to be viable in real-world applications.

Designing for ultra-low energy
Many of the MCUs targeting the IoT are based on the same CPU technology, the ARM Cortex-M processors. These come in various forms, ranging from the Cortex-M0+, optimised for cost and power efficiency for simpler applications, to the Cortex-M4, which is excellent for complex, high-performance applications requiring floating point and DSP operations.

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