From manual testing to AI-powered optimization: How modern power profiling tools enable all-day devices

The global market for wearable devices is predicted to exceed $186 billion by 2030, with fitness trackers to augmented reality glasses, on Android, accounting for most of that amount. But the greater confinement for engineers like Dmitrii to work within, the higher the requirements for energy efficiency and response speed is becoming. Today, the main breakthroughs are not coming from new innovations in battery cells, but from the tools that Dmitrii and his team are coming up with to manage every precious milliwatt. 

Against the background of this trend, devices are no longer just accessories – they are becoming mobile computing platforms that require serious engineering solutions in terms of performance, autonomy, responsiveness and sustainability.

Across the industry, companies are rethinking how power usage is measured and optimised. In recent years, next-generation smart glasses and other advanced wearables have demonstrated significant gains in battery life compared to earlier models – not because of larger batteries, but due to changes in profiling methodology and optimisation strategy. These improvements reflect a broader shift: engineers are moving away from coarse, manual testing toward continuous, data-driven analysis of energy consumption.

Earlier workflows often relied on external power monitors, ad-hoc scripts, and manual data aggregation in spreadsheets – processes that could take days to produce a single usable result. Modern approaches focus on fine-grained visibility into how software decisions affect power draw in real time. The goal is not only to extend battery life, but to intelligently trade off energy efficiency against latency, user experience, and device expectations within tight physical constraints.

In this context, power profiling tools have become as critical as processors or batteries themselves. Through this technical discussion, Dmitrii explains how contemporary wearables are evolving and why the future of all-day devices depends less on hardware breakthroughs and more on how engineers understand and optimize energy at every layer of the system.

The wearable technology landscape

Modern wearable devices have long ceased to be just fitness trackers. These devices have evolved into constantly active sensor platforms that read biometric data, interpret movements, stream audio and video, and apply augmented reality elements. All of this requires a new approach from engineers: to make devices smaller, smarter and more autonomous, without reducing responsiveness and convenience. It may seem simple, but it is not easy to fit a full-fledged phone with powerful features into a watch case or glasses frame, and this presents a fascinating challenge for Dmitrii who says “Designing wearable devices is no longer a matter of reducing hardware, but of scaling intelligence. The system must understand when to calculate, what to measure, and what can be postponed without compromising the user experience.” 

Unlike smartphones, wearable devices exist within an energy budget of less than 1 watt. CPUs, GPUs, DSPs, radio modules, and sensors are all competing for this finite resource. Even the Android system services – telemetry, logs, notifications – can cause hundreds of micro-awakenings per minute. Consequently, each milliwatt becomes an engineering parameter. Any engineering solution like CPU frequency, memory access, sensor polling frequency, affects both user comfort and battery life. Therefore, along with latency and bandwidth, a third parameter has become a key focus: power as an indicator of performance. Dmitrii notes that in the age of wearable devices, productivity is becoming a task of allocating resources in time and energy: engineers do not have the luxury of idle processes. Every system must start, run, and stop like clockwork.

To meet such requirements, the industry is implementing a new generation of context-aware operating systems and diagnostic frameworks. They are designed to recognise user intent, predict workload patterns, and dynamically reconfigure system states in real time. Android-based platforms now integrate on-board power sensors, continuous profiling, and regression detection directly into CI/CD workflows – allowing developers to correlate code commits with real-world battery impact.

These innovations are not just a set of technologies, but a change in engineering thinking: from single optimization to continuous measurement and improvement. This is how a new generation of wearable devices is being formed – not just functional, but durable, where the rational use of energy is becoming the main sign.

How is the operation of wearable devices optimized

Unlike visual computing devices such as glasses, where throughput is the main challenge, low-latency systems like neural wristbands or motion controllers operate under much tighter timing constraints. They must respond within milliseconds to maintain a natural sense of interaction.

To make such devices work efficiently, companies on wearable devices are equipped with built-in power meters and trace-based analysis frameworks, for example, system tracing tools to visualize performance and energy events across the entire OS stack. Instead of manually connecting expensive meters, engineers now use such tools together with in-house extensions such as the Power Context Aware Tool and AI Historian – integrations led by Dmitrii Volykhin that allow power and performance data to be analyzed directly in the CI/CD cycle. The system automatically collects data on consumption and detects regressions: if power consumption has increased by at least 5-10% after the update, the algorithm will record this within a few hours. Dmitrii notes that the transition from manual measurements to automated analysis transforms optimization from guesswork into statistics. This is the only way to scale energy efficiency.

The next stage of optimization is predictive architecture. “If the user raises his glasses to take a photo, the system must activate image processing in advance and put the other subsystems to sleep. This is not a side effect, but deliberate resource orchestration,” explains the engineer. It is this combination of hardware telemetry, Android internals and analysis automation that forms a new generation of wearable technologies – devices that not only execute commands, but understand the context and manage energy as intelligence.

The new era of power testing

Behind every new version of energy-efficient wearable devices, there is a revolution not in hardware, but in tools. For Dmitrii Volykhin, the path began with a simple principle: you cannot optimize something that cannot be measured. “When you can measure the behavior of a system to the millisecond, optimization stops being an experiment and turns into an engineering task,” he explains.

When Dmitrii joined a large-scale wearables engineering organisation, power testing was accurate but heavily manual. The approach worked for isolated experiments, yet proved incompatible with modern product cycles. There was no unified, automated system for collecting power data. Engineers physically connected external power monitors – specialized equipment costing tens of thousands of dollars – to devices at their desks, ran custom scripts, and manually transferred results into spreadsheets. Each test scenario could take hours or even days, and validating a single feature across multiple power states sometimes stretched to an entire week.

As wearable devices grew more complex and product timelines shortened, this workflow became a clear bottleneck. Devices were expected to deliver longer battery life, instant responsiveness, and stable performance – all within severe physical and thermal constraints. Something had to change.

This led to the creation of an integrated power monitoring tool. Instead of relying on external equipment, devices began recording current and voltage internally during automated test runs. The impact was dramatic: the cost per test dropped from hundreds of pounds to just a few, while test coverage expanded to nearly the entire codebase. Power measurement became accessible to every developer, executable with a single terminal command rather than specialised hardware and manual setup.

The next step was automating interpretation, not just collection of data. On top of this monitoring infrastructure, a regression detection platform was introduced. It analyses thousands of power-consumption time series generated by continuous integration pipelines and automatically detects deviations from established baselines. Statistical models flag anomalies: for example, a 10% increase in power usage during camera activity, and link them directly to the responsible code change.

At the core of the system lies a correlation engine that matches operating-system-level activity with spikes in energy consumption. If a new commit increases current draw, introduces excessive CPU wake-ups, or alters power behaviour in a critical subsystem, the system localises the issue and triggers corrective action before release. “Energy regressions are the same bugs, only they used to appear on devices. Now we catch them overnight,” Volykhin notes.

By unifying precise measurement with automation, these frameworks transformed wearable development from reactive debugging to predictive optimisation. Engineers can now model the energy impact of upcoming features, simulate battery behaviour before implementation, and validate efficiency continuously throughout development. Battery life has effectively become a software quality metric – as fundamental as latency or memory usage. Instead of waiting for breakthroughs in battery chemistry, teams achieved compounding gains through tooling, process, and shared responsibility. Every engineer became to some extent a power engineer.

The next decade

Battery technology improves at 5-8% annually while processors double in capability every two years. By 2030, processors will be eight times more powerful; batteries perhaps 50% better. The data-driven approach suggests radical efficiency through intelligent software management rather than throttling or accepting poor battery life.

Now energy has become a first-class citizen. Software architects debate milliwatt costs. Product managers weigh user value against battery budgets. QA tracks energy regressions like crash rates. Complex AI models run only when needed. Background tasks batch efficiently. Sensors adjust sampling dynamically.

A generation of engineers trained on these tools will think about energy from day one, instinctively batching operations and profiling consumption. As these methodologies spread through conference talks and job changes, the baseline for wearable battery life shifts industry-wide.

“The industry has been waiting decades for a breakthrough in battery technology. But if you look at the history of tech, breakthroughs more often come from smart use of what we already have. We can’t change the physics of lithium-ion batteries, but we can radically change how we spend every milliwatt,” Dmitrii commented.

The companies that dominate the next decade won’t have the best batteries or most efficient chips. They’ll be those who learned that when processing power doubles biennially but battery capacity crawls forward, the only sustainable strategy is applying rigorous optimization – the kind that once saved millions in advertising costs – to saving milliwatts in devices.

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