Using an ultra low power microcontroller alas does not by itself mean that an embedded system designer will automatically arrive at the lowest possible energy consumption.  To achieve this, the important role of software also needs to be taken into account.  Code needs to be optimized, not just in terms of functionality but also with respect to energy efficiency.  Software has perhaps never really been formally identified as an ‘energy drain’ and it needs to be.  Every clock cycle and line of code consumes energy and this needs to be minimized if best possible energy efficiency is to be achieved.

While the first two parts of this article have proposed the fundamental ways in which microcontroller design needs to evolve in the pursuit of real energy efficiency, so this third part considers how the tools which support them also need to change.  Having tools available that provide developers with detailed monitoring of their embedded systems’ energy consumption is becoming vital for many existing and emerging energy sensitive battery-backed applications.

As a development process proceeds, code size naturally increases and optimizing it for energy efficiency becomes a much harder and time-consuming task.  Without proper tools, identifying a basic code error such as a while loop that should have been replaced with an interrupt service routine can be difficult.  Such a simple code oversight causes a processor to stay active waiting for an event instead of entering an energy saving sleep mode – it therefore matters!  If these ‘energy bugs’ are not identified and corrected during the development phase then they’re virtually impossible to detect in field or burn-in tests.  Add together a good collection of such bugs and they will have an impact on total battery lifetime, perhaps critically so.

In estimating potential battery lifetimes, embedded developers have been able to use spreadsheets provided by microcontroller vendors to get a reasonable estimation of application behavior in terms of current consumption.  Measurements of a hardware setup made by an oscilloscope or multimeter and entered into spreadsheets can be extrapolated to give a pretty close estimation of battery life expectancy.  This approach however does not provide any correlation between current consumption and code and the impact of any bugs – the application’s milliamp rating is OK, but what’s not known is whether it could be any better.

With a logic analyzer a developer gets greater access to the behavior of the application and begins to recognize that ‘something strange’ is going on.  Yes it’s a ‘code view’ tool, and shows data as timing diagrams, protocol decodes, state machine traces, assembly language or its correlation with source level software, however it offers no direct relationship with energy usage.

Combine the logic analyzer, the multimeter, and the spreadsheet and you do start to make a decent connection between energy usage and code, but the time and effort spent in setting up all the test equipment (and possibly repeating it identically on numerous occasions), making the measurements and recording them into spreadsheets can be prohibitive if not practically impossible.

Low power processors such as the ARM Cortex-M3 however are already providing a SWO (serial wire output) that can be used to provide quite sophisticated and flexible debug and monitoring capabilities that tool suppliers can harness to enable embedded developers to directly correlate code execution with energy usage.

Simple development platforms can be created which permanently sample microcontroller power rail current consumption, convert it, and send it along with voltage and timing data via USB to a PC-based energy-to-code profiling tool.  Courtesy of the ARM’s SWO pin, the tool can also retrieve program counter information from the CPU.  The coming together of these two streams of data enables true ‘energy debugging’ to take place.

Provided that current measurements have a fairly high dynamic range, say from 0.1µA to 100mA, then it’s possible to monitor a very wide and practical range of microcontroller current consumption.  Once uploaded with the microcontroller object code, the energy profiling tool then has all the information resources it needs to accurately correlate energy consumption with code.

The energyAware Profiler tool from Energy Micro shows the relationship between current consumption, C/C++ code, and the energy used by a particular function. Clicking on a current peak, top right, reveals the associated code, bottom left.

The tool correlates the program-counter value with machine code, and because it is aware of the functions of the C/C++ source program, it can then readily indicate how energy use changes as various functions run.  So the idea of a tool that can highlight to a developer in real time, an energy-hungry code problem comes to fruition.  The developer watches a trace of power versus time, identifies a surprising peak, clicks on it and is immediately shown the offending code.

Such an ability to identify and rectify energy drains in this way and at an early stage of prototype development will certainly help reduce the overall energy consumption of the end product, and it will not add to the development time either, on the contrary.

We would all be wise to consider the debug process of low power embedded systems development as becoming a 3-stage cycle from now on:  hardware debugging, software functionality debugging, and software energy debugging.

Microcontroller development tools need to evolve to enable designers to identify wasteful ‘energy bugs’ in software during the development cycle.  Discovering energy-inefficient behavior that endanger battery lifetime during a product field trial is after all rather costly and really just a little bit too late!

While using a 32-bit processor can enable a microcontroller to stay in a deep-sleep mode for longer, there is nevertheless some baseline power consumption which can significantly influence the overall energy budget. However, historically 32-bit processors have admittedly not been available with useful sub-µA standby modes. With the introduction of power efficient 32-bit architectures, the standby options are now complementing the reduced processing and active time.

With the relatively low power consumption many microcontrollers exhibit in deep sleep, the functionality they provide in these modes is often very limited.  Since applications often require features such as real time counters, power-on reset / brown-out detection or UART reception to be enabled at all times, many microcontroller systems are prevented from ever entering deep sleep since such basic features are only available in an active run mode.  Many microcontroller solutions also have limited SRAM and CPU state retention in sub-µA standby modes, if at all.  Other solutions need to turn-off or duty-cycle brown-out and power-on reset detectors in order to save power.

In the pursuit of energy efficiency then microcontrollers need to provide product designers with a choice a sleep modes offering the flexibility to scale basic resources, and thereby the power consumption, in several defined levels or energy modes.  While energy modes constitute a coarse division of basic resources, additional fine-grained tuning of resources within each energy mode should also be able to be implemented by enabling / disabling individual peripheral functions.

There’s little point though in offering a microcontroller with tremendous sleep mode energy consumption if its energy efficiency gains are lost due to the time it takes for the microcontroller to wake up and enter run mode.

When a microcontroller goes from a deep sleep state, where the oscillators are disabled, to an active state, there is always a wake-up period, where the processor must wait for the oscillators to stabilize before starting to execute code.  Since no processing can be done during this period of time, the energy spent while waking up is wasted energy, and so reducing the wake-up time is important to reduce overall energy consumption.

Furthermore, microcontroller applications impose real time demands which often mean that the wake-up time must be kept to a minimum to enable the microcontroller to respond to an event within a set period of time.  Because the latency demanded by many applications is lower than the wake-up time of many existing microcontrollers, the device is often inhibited from going into deep sleep at all – not a very good solution for energy sensitive applications.

A beneficial solution would be to use a very fast RC oscillator that instantly wakes up the CPU and then optionally transfers the clock source to a crystal oscillator if needed. This meets both the real time demands as well as encourages run- and sleep mode duty cycling. Albeit the RC oscillator is not as accurate as a crystal oscillator, the RC oscillator is sufficient as the CPUs clock source during crystal start-up.

We know that getting back to sleep mode is key to saving energy. Therefore the CPU should preferably use a high clock frequency to solve its tasks more quickly and efficiently.  Even if the higher frequency at first appears to require more power, the advantage is a system that is able to return to low power modes in a fraction of the time.

Peripherals however might not need to run at the CPU’s clock frequency.  One solution to this conundrum is to pre-scale the clock to the core and peripherals, thereby ensuring the dynamic power consumption of the different parts is kept to a minimum.  If the peripherals can further operate without the supervision of the CPU, we realize that a flexible clocking system is a vital requirement for energy efficient microcontrollers.

The obvious way for microcontrollers to use less energy is to allow the CPU to stay asleep while the peripherals are active, and so the development of peripherals that can operate with minimum or no intervention from the CPU is another worthy consideration for microcontroller designers.  When peripherals look after themselves, the CPU can either solve other high level tasks or simply fall asleep, saving energy either way.

With advanced sequence programming, routines for operating peripherals previously controlled by the CPU can be handled by the peripherals themselves.  The use of a DMA controller provides a pragmatic approach to autonomous peripheral operation.  Helping to offload CPU workload to peripherals, a flexible DMA controller can effectively handle data transfers between memory and communication or data processing interfaces.

Of course there’s little point in using autonomous peripherals to relieve the burden of the CPU if they’re energy hungry.  Microcontroller makers also need to closely consider the energy consumption of peripherals such as serial communication interfaces, data encryption/decryption engines, display drivers and radio communication peripherals.  All peripherals must be efficiently implemented and optimized for energy consumption in order to fulfill the application’s need for a low system level energy consumption.

Taking the autonomy ideal a step further, the introduction of additional programmable interconnect structures into a microcontroller enable peripherals to talk to peripherals without the intervention of the CPU, thereby reducing energy consumption even further.  A typical example of a peripheral talking to another peripheral would be an ADC conversion periodically triggered by a timer. A flexible peripheral interconnect allows direct hardware interaction between such peripherals, solving the task while the CPU is in its deepest sleep state.

The third part of this three part article explores the tools and techniques available for energy debugging.