Low Power Electronics Design: Techniques for Battery-Optimised Products

Did you know low power design techniques can reduce energy consumption in embedded systems by up to 70%? With the growing demand for battery-operated devices and environmental concerns, optimising power usage has become crucial for modern electronics. However, achieving significant power reductions requires a systematic approach that spans both hardware and software domains.

Power efficiency isn’t simply about extending battery life; it’s also about reducing heat generation, improving reliability, and meeting strict regulatory requirements. Throughout this article, we’ll explore proven strategies for minimising power consumption across different system levels. Specifically, we’ll examine hardware techniques involving component selection and circuit design, software optimisations that reduce processing overhead, energy-efficient communication protocols, and comprehensive system-level power management approaches.

By implementing these techniques, you’ll be able to create more efficient embedded systems that not only consume less power but also perform more reliably in the field. Whether you’re designing IoT devices, wearable technology, or industrial equipment, these power-saving methods will help you achieve better performance while significantly reducing energy requirements.

Power consumption in embedded systems is fundamentally divided into two categories: static and dynamic power. Understanding these components is essential for engineers seeking to create energy-efficient designs that maximise battery life and minimise heat generation.

Static power, often called leakage power, occurs even when a circuit is idle with no switching activity. This power consumption stems from leakage currents present in active circuits regardless of clock rates or usage scenarios. As transistor sizes shrink below 90nm, leakage power has become the dominant consumer of power, whilst in larger geometries, dynamic power contributes more significantly.

The static power consumption can be calculated using the formula: Pstatic = Vcc × Icc

Where Vcc represents the supply voltage and Icc represents the overall current flowing through the device from leakage currents ³. Engineers can reduce static power by disconnecting portions of the circuit from the supply voltage or by reducing the supply voltage itself.

In contrast, dynamic power occurs during active circuit operation and results from two primary sources:

1. Switching power (capacitive load charging/discharging)
2. Short-circuit power

The dynamic power consumption follows the formula: Pdynamic = a × f × Ceff × Vdd²

Where ‘a’ represents switching activity, ‘f’ is the clock frequency, ‘Ceff’ is the effective capacitance, and ‘Vdd’ is the supply voltage. Notably, this equation reveals why voltage reduction has such a powerful effect on power consumption—the voltage term is squared, meaning even small reductions yield significant power savings.

Short-circuit power consumption occurs during transistor switching transitions. As the input voltage changes, both the PMOS and NMOS transistors in a CMOS gate are momentarily conductive simultaneously, creating a direct path from the supply voltage to ground. This phenomenon accounts for approximately 10-15% of the total power consumption in CMOS circuits.

Short-circuit current typically peaks when the input voltage is near the midpoint of its transition, when both transistors are weakly on ⁴. The analytical expression for short-circuit power is: Pshort-circuit = Isc × Vdd × f

Where Isc represents the short-circuit current during switching. Consequently, this power component increases with higher operating frequencies and supply voltages.

The relationship between power and energy is critical yet frequently misunderstood. Energy consumption (measured in Joules) is the integral of power dissipated over time: E = ∫ P dt

For a constant power level, this simplifies to: E = P × T

Where T represents the active period. This relationship explains an important principle: reducing power consumption saves energy only if execution time doesn’t increase proportionally.

When engineers reduce dynamic power by lowering clock frequency, execution times typically increase. If the time increase is proportional to the power decrease, the energy consumption remains essentially unchanged. Furthermore, during extended execution times, static leakage accumulates, potentially increasing overall energy consumption.

For genuine energy savings, designers must ensure that:

1. Power reduction exceeds execution time increase, or
2. Power is reduced without extending execution time

This principle is why voltage scaling (which affects power quadratically) is often more effective than frequency scaling alone. Additionally, completing tasks quickly at higher power and then entering deep sleep modes can sometimes be more energy-efficient than prolonged operation at lower power levels.

Understanding these fundamentals provides the foundation for implementing the practical power-reduction techniques we’ll explore in subsequent sections.

Hardware design choices have the greatest impact on reducing power consumption in embedded systems. By combining several targeted techniques, engineers can achieve power reductions of up to 70% without sacrificing performance. Let’s examine the five most effective hardware-based approaches.

Quiescent current (Iq) refers to the power a component draws when idle or not actively performing its primary function. This seemingly minor parameter becomes crucial for battery-powered devices that spend most of their time inactive.

Modern low quiescent current LDO (Low-Dropout) regulators consume as little as 300 nA during standby, compared to microamps or milliamps in standard regulators. These regulators are particularly valuable in portable consumer devices like smartphones, smartwatches, and wearables where they help extend battery life without compromising performance.

For optimal results, select components with:

• Quiescent current in the nanoamp range
• High conversion efficiency voltage regulators
• Low leakage passive components

The impact is substantial—in devices that spend significant time in idle mode, switching to components with nanoamp-level quiescent current can reduce power consumption by 60% compared to those with even modest 1.5μA requirements.

Deep sleep mode represents the lowest power state available in modern microcontrollers, where virtually all functions are disabled except those necessary for wake-up.

In this mode, microcontrollers:

• Disable system clock sources and most peripherals
• Maintain only essential functions like RTCC (Real-Time Clock Calendar) and watchdog timers
• Reduce power consumption to as low as 20nA
• Allow wake-up via specific triggers like external interrupts or timers

Microcontrollers with eXtreme Low Power (XLP) technology can enter deep sleep with or without memory retention. Memory retention preserves RAM contents but consumes slightly more power, whereas non-retention modes preserve only specific registers but use even less power.

The wake-up time trade-off must be considered—deep sleep modes typically require 2-8μs to resume operation, whereas complete shutdown modes may need 200μs or more.

Modern sensors with built-in power management features automatically transition to low-power states when inactive, dramatically reducing system power consumption.

Effective sensors for low-power designs include:

• Components with quick start-up times to minimise active power states
• Sensors featuring programmable sampling rates
• Devices with built-in auto-sleep functionality that consumes ≤1μA in sleep mode

Radar modules with auto-sleep capability, for instance, can significantly reduce power consumption in smart home applications when no motion is detected. Similarly, environmental sensors with multiple power states (deep sleep, light sleep, and standby) optimise power usage based on activity levels.

Clock gating is a powerful technique that selectively turns off clock signals to inactive portions of a digital circuit, eliminating unnecessary switching activity.

Given that clock distribution networks often account for 30-50% of total switching power in ASICs and SoCs, selectively disabling clocks can yield substantial savings. Properly implemented clock gating can reduce power usage of sequential logic by 20-50% in many designs.

The approach works by:

• Using gates (typically AND or OR gates) to control clock propagation
• Enabling clocks only when the associated logic needs to operate
• Preventing unnecessary toggling of flip-flops and registers

For maximum effectiveness, clock gating should be implemented at multiple levels—from individual flip-flops to entire functional blocks and peripheral modules.

Dynamic Voltage Scaling adjusts both operating voltage and frequency based on processing requirements. Since power consumption scales quadratically with voltage (P = CV²F), even small voltage reductions yield significant power savings.

The technique operates on these principles:

• The system predicts immediate processing needs
• It adjusts clock frequency to match required performance
• It then reduces supply voltage to the minimum needed to maintain that frequency
• This combination reduces switching power exponentially rather than linearly

DVS can reduce power consumption by a factor of 8 when both voltage and frequency are halved. This approach is particularly effective in systems with varying workloads, such as mobile devices and battery-operated equipment.

For implementation, the power supply must be able to adjust output voltage dynamically based on digital input from the processor while maintaining stability. Modern processors and SoCs employ aggressive DVS at many levels—from functional units to entire peripheral blocks—to achieve maximum power efficiency.

While hardware selection forms the foundation of low power design, software optimisation offers equally significant opportunities for energy reduction. Effective code and execution strategies can diminish power consumption even when using identical hardware components.

Compiler optimisations significantly impact both performance and energy consumption. Nevertheless, not all optimisations yield power efficiency—some may actually increase power usage depending on programme characteristics. Studies show that power-aware compiler optimisation approaches like COSPpp (a case-based reasoning approach) can achieve a balanced performance and power efficiency improvement of approximately 7%. Modern compilers consider several factors:

• Reducing instruction count and execution time
• Minimising memory accesses
• Optimising register usage to decrease power-hungry memory operations
• Loop unrolling and code rearrangement for energy efficiency

These optimisations must remain correct, reasonable in compilation time, and maintain the programme’s functionality.

Advanced scheduling algorithms prioritise both performance and energy conservation. The power-aware best-effort task scheduling algorithm (PA-BTA) optimises real-time performance while reducing power consumption. This approach employs a metric called “energy and real-time performance grade” (ERG) to evaluate schedules holistically.

Power-aware scheduling particularly benefits from dynamic voltage scaling, as the energy consumed is proportional to the clock frequency and to the square of the voltage applied to the processor core. By intelligently reducing both parameters while ensuring deadlines are met, these algorithms can decrease power consumption without compromising system response.

The choice between interrupt-driven and polling-based approaches affects power consumption markedly. With polling, the CPU repeatedly checks device status in a continuous loop, consuming cycles even when devices are idle. Conversely, interrupt-driven systems allow the CPU to perform other tasks or enter sleep modes until explicitly signalled by peripheral devices.

Interrupts generally offer better power efficiency except in cases where devices require extremely frequent attention. The mathematical relationship indicates that interrupts are more efficient when the probability of a device needing service (P) is low.

Direct Memory Access (DMA) enables data transfer without CPU intervention, allowing the processor to remain in sleep mode during peripheral operations. This approach can reduce power consumption impressively—for example, using DMA instead of processor-driven transfers for serial port data has shown power reductions of up to 20%.

On microcontrollers supporting autonomous DMA operations, transfers can occur even in low-power modes like Stop 0 and Stop 1, with the DMA controller managing its own clock gating. This capability is particularly valuable for applications requiring periodic sensor sampling or communications while maintaining minimal power usage.

Selecting the appropriate communication interfaces plays a vital role in creating energy-efficient embedded systems. The right protocol can mean the difference between hours and years of battery life.

Each standard wired protocol offers distinct power profiles suitable for different applications. UART demonstrates the lowest power consumption among these three, requiring only two data lines without clock signals for asynchronous operation. This makes it ideal for simple device-to-device communication with minimal energy requirements. I2C utilises a two-wire interface (SDA and SCL) and consumes less power than SPI due to its slower data rates, yet slightly more than UART. Meanwhile, SPI delivers the highest speeds but at the cost of increased power consumption due to its high-frequency clock signals and multiple communication lines (MOSI, MISO, SCK, SS).

For wireless communications, protocol selection substantially impacts energy usage. BLE operates in the 2.4 GHz band with ultra-low power consumption, remaining in sleep mode except when connections are initiated. This makes it perfect for wearable fitness trackers and health monitors requiring extended battery life. ZigBee also operates primarily in the 2.4 GHz band but employs mesh networking where nodes relay messages. While this extends coverage, it can increase power usage compared to BLE. LoRa stands out for extremely low power consumption coupled with impressive range—up to 10-15 kilometres in rural areas and 1-5 kilometres in urban environments.

RF transmission characteristics directly affect power consumption. Crucially, lower frequency signals travel further with less power due to better propagation characteristics. This explains why systems like military communications use VLF (Very Low Frequency) for long-distance transmission with minimal power. Additionally, reducing transmission frequency and power allows devices to operate longer on batteries, though this must be balanced against range requirements. Thoughtfully scheduling transmissions and minimising their duration further improves energy efficiency in battery-operated devices.

System-level power management integrates various techniques into a cohesive strategy, enabling embedded designs to achieve maximum efficiency. By orchestrating power consumption across the entire system, engineers can extend battery life from hours to years.

Power Management Integrated Circuits (PMICs) manage power across electronic devices with varying voltage requirements. These specialised components enable power domain isolation—a technique that separates circuits into independently controlled regions. Modern PMICs contain multiple features:

• Single or multiple DC-to-DC converters (buck or boost)
• Low-dropout regulators (LDO)
• Power FETs with low resistance to minimise power loss
• Real-time clocks for timing functions

When properly implemented, isolation cells prevent floating signals from propagating from powered-down blocks to active domains ²⁴. This approach allows non-essential sections to be completely powered down without affecting critical functions.

Duty cycling—the process of removing power from components when not needed—forms the cornerstone of effective power management. With negligible OFF power, average power consumption becomes directly proportional to the duty cycle. For instance, a system operating at 10% duty cycle consumes merely 10% of its normal power.

The effectiveness depends on measurement cycles comprising start-up time, settling time, and data acquisition time. Some devices offer sleep modes that maintain initialisation values while shutting down other systems, enabling faster recovery times albeit with slightly higher power consumption.

FIFO (First-In-First-Out) buffers dramatically reduce system power by allowing processors to remain in sleep mode while data accumulates. As an illustration, a 32-sample FIFO buffer enables the host processor to sleep longer between data collections. This approach particularly benefits systems collecting periodic sensor data.

Calculations show that reading all 32 samples at once (4.878ms) is more efficient than multiple smaller reads. For systems sampling at 200Hz, configuring the watermark at 31 samples provides sufficient wake-up time while maximising sleep duration.

Battery-aware firmware implements intelligent power management strategies within code execution. Effective patterns include programmatic control of sleep modes, automatic transitions between power states based on activity levels, and strategic use of deep sleep modes. Practical implementations might involve time-keeping devices like RTCs that wake the system after programmed timeouts, thereafter allowing processors to operate in nanoampere current ranges during inactive periods.

Achieving significant power reductions in embedded systems demands a holistic approach across hardware, software, and system design. Throughout this article, we explored numerous techniques that collectively enable power consumption reductions of up to 70%. Hardware optimisations clearly provide the foundation for energy efficiency through component selection and circuit design techniques. Low quiescent current components, microcontrollers with advanced sleep modes, and dynamic voltage scaling stand out as particularly effective methods.

Software strategies further enhance these hardware foundations. Power-aware scheduling algorithms, interrupt-driven execution, and strategic DMA implementation work together to minimise unnecessary processor activity. Additionally, communication protocol selection plays a crucial role in overall system efficiency, with each option offering distinct advantages for specific applications.

System-level strategies tie everything together by orchestrating when and how different components operate. Duty cycling, power domain isolation, and FIFO buffering serve as powerful tools for managing energy consumption across the entire system. Battery-aware firmware design patterns likewise ensure software actively participates in power conservation efforts.

The combined implementation of these techniques yields benefits beyond mere battery life extension. Reduced heat generation improves reliability, smaller power supplies decrease device size, and lower energy consumption helps meet increasingly stringent regulatory requirements. Engineers who master these power-saving approaches gain a significant competitive advantage in today’s market where energy efficiency remains a critical differentiator.

Future embedded systems will undoubtedly face even greater pressure to maximise performance while minimising power consumption. Though challenging, this balance becomes achievable through systematic application of the techniques outlined here. The path to truly efficient embedded systems certainly requires careful consideration at every design stage, but the rewards—extended battery life, improved thermal performance, and enhanced reliability—make these efforts worthwhile.