Four Power Management Strategies for Battery-Bound Edge Devices

The need to fit machine learning (ML) solutions into battery-powered devices has created a new kind of engineering standoff. ML models are hungry, and their computational needs grow exponentially. At the same time, battery improvements crawl forward in a slow, linear line. As a result, you can’t just throw in a bigger battery.

Today, power management is not a background task. It’s a core challenge for engineering leaders building the next generation of IoT edge devices, especially in healthcare edge computing, where reliability and runtime are critical.

Simply keeping these devices "always on" – listening, sensing, and analyzing – pushes power limits too far. The consequence is shorter battery life and unwanted heat buildup. Meeting this challenge requires an integrated approach where hardware and software evolve together. Success depends on a strategy that stretches from silicon to software to the battery itself.

Below are four key strategies to help engineering teams design energy-efficient, high-performance ML devices that last longer and run faster.

Strategy 1: Synchronizing Hardware and Software

The foundational layer of any effective power-management strategy for edge computing devices lies in hardware-software co-design. Instead of treating them as separate layers, this approach fosters a dynamic partnership in which software actively leverages the hardware’s power-saving capabilities.

Providing the Power-Saving Levers for Edge Devices

Modern System-on-Chips (SoCs) come equipped with a rich toolkit of power-saving features. These are the physical levers that software can pull to balance performance and consumption in edge AI devices:

• Dynamic Voltage and Frequency Scaling (DVFS) adjusts a processor’s voltage and frequency in real time. When computational demand drops, so does energy use, ensuring power is spent only when needed.
• Clock and Power Gating. Clock gating halts unnecessary activity by disabling the clock signal to idle modules, while power gating cuts power entirely to inactive chip blocks, reducing leakage current and idle drain.
• Specialized Accelerators like GPUs, Tensor Processing Units (TPUs), Neural Processing Units (NPUs), or custom ASICs perform ML-specific computations, such as matrix multiplications, far more efficiently than general-purpose CPUs.

Pulling the Levers with Software

Hardware alone can’t optimize itself. Software must act as the orchestrator, managing when and how to use those hardware levers.

• Power-Aware Scheduling allows the OS or RTOS to assign tasks to the most efficient cores, dynamically adjusting DVFS levels to match workload demands. Low-priority tasks can run on low-power cores while critical ML inferences use performance-optimized ones.
• Runtime Monitoring and Adaptation form a feedback loop. Software tracks system load, thermal conditions, and battery levels, then fine-tunes power settings accordingly.
• Firmware and Driver Development play a crucial role in exposing the hardware’s power features to the software stack. Well-crafted firmware and drivers are the connective tissue that translates system insights into power control.

Machine learning at the edge doesn’t run in neat, predictable cycles. Instead, it comes in bursts — short, intense computations followed by long idle stretches. Static power optimization can’t handle that volatility. The system must ramp up quickly, perform the inference, then slip back to near-zero power. This is where software expertise becomes decisive: OS-level programming, driver tuning, and real-time analytics determine whether an edge device drains its battery or conserves it.

Strategy 2: Right-Sizing ML Models for Edge Devices

The biggest power draw in any ML-enabled edge device isn’t the screen or sensors; it’s the model itself. That makes model optimization not a luxury, but a necessity. The challenge is to take a model trained in the comfort of a cloud data center and reshape it to thrive within the tight energy and memory limits of a battery-powered edge computing device.

Model Compression: Reducing Size & Complexity

The goal is simple — reduce size and complexity while preserving accuracy. Achieving it requires three complementary techniques.

• Pruning systematically removes parts of a neural network that contribute little to accuracy. Unstructured pruning deletes individual weights, creating sparse networks that shine on hardware designed for sparse computations. Structured pruning takes a bolder step by removing entire neurons or channels, creating smaller, denser models that run faster on standard processors. Done right, pruning cuts both size and inference time dramatically.
• Quantization goes after numerical precision. Models are usually trained in 32-bit floating point. Converting to 16-bit floats (or more aggressively, to 8-bit integers) shrinks the memory footprint by up to 75%. Even better, integer arithmetic is far more efficient on most embedded processors, meaning faster and cooler inference. For IoT edge devices, this can be the difference between minutes and hours of additional runtime.
• Knowledge Distillation takes inspiration from teaching itself. A large, sophisticated “teacher” model transfers its learned behavior to a smaller “student” version. The student, simpler but well-trained, can achieve similar accuracy in a fraction of space and power. This technique has become central to deploying ML models on edge devices, especially where accuracy is non-negotiable, such as AI and ML in medical devices (see real-world healthcare use cases here).

Architecture & Frameworks for ML on Edge Devices

Optimization starts with the right foundation. Trying to retrofit a data center-scale model for the edge is like forcing a supercomputer into a smartwatch. Instead, begin with architectures designed specifically for efficiency — MobileNets, EfficientNets, and the broader TinyML family of algorithms. These are purpose-built for edge AI devices, capable of running complex inferences on microcontrollers that sip power in milliwatts.

To bring these models to life, developers rely on edge-optimized frameworks like TensorFlow Lite and PyTorch Mobile, which leverage hardware acceleration while managing tight compute budgets. The result: real-time intelligence without real-time drain.

The final step is translating the trained model into a binary that fits the embedded target. High-level development frameworks like Python are invaluable for design and training, but must eventually yield to the constraints of C++ or similar low-level environments.

A case in point: the OtoNexus Novoscope, a medical edge device designed for near-real-time diagnostic analysis. Our team developed a custom script that automatically generated optimized C++ class structures from Python-based models. This translation preserved ML accuracy while meeting strict performance and power targets — a prime example of how deep optimization turns theoretical ML into practical, deployable models.

Strategy 3: Dynamic System Management on Edge Devices

Beyond optimizing the primary workload, significant power savings can be achieved by managing the device's runtime behavior, particularly during periods of inactivity. The core principle here is to maximize the time spent in low-power sleep states.

Event-Driven Architecture for Edge AI Devices

A well-optimized edge device should sleep as much as possible. That means designing an event-driven, sleep-centric architecture, where activity is the exception, not the rule.

• Duty Cycling: The system stays in deep sleep for most of its life, waking only for short, scheduled tasks like taking a sensor reading or running an inference, and then immediately returning to rest. This simple rhythm of sleep and work dramatically reduces average power use in IoT edge devices.
• Event-Driven Processing: Instead of constantly checking for new input (which keeps the processor awake), the device waits for external triggers. An accelerometer detecting motion or a low-power audio unit hearing a wake word can activate the main processor only when needed. This ensures that energy-hungry cores stay quiet until they truly have work to do.

State Machines

A well-defined state machine is the backbone of a reliable and power-efficient embedded system. It governs transitions between power states, ensuring the system always uses just enough energy. Typical states include:

• Active/Run: The system operates at full power for demanding ML inference or data processing.
• Idle: The CPU clock halts, but RAM and key peripherals stay on for instant wake-up.
• Standby/Sleep: Most of the hardware shuts down, leaving minimal circuits active to detect wake signals — ideal for low-duty edge computing devices.
• Charging: A controlled state for managing battery charge and temperature.
• Low Battery: A safeguard mode that disables non-essential features to preserve mission-critical functionality.

Power management isn’t just about saving energy — it’s about understanding when to spend it wisely. If a device wakes up too frequently for very short tasks, the cumulative energy cost of these transitions can outweigh the energy saved by sleeping.

This leads to a non-obvious optimization challenge: it can sometimes be more energy-efficient to batch several small tasks together and stay awake for a slightly longer, single period than to perform many rapid sleep/wake cycles. Engineering teams must profile not only the power consumption of each state but also the energy cost of the transitions between them.

This approach made all the difference when our engineers worked on the Novoscope project, helping our client turn PoC into a production-ready medical edge device for clinical diagnostics. Its finely tuned state machine enabled seamless transitions between charging, standby, and low-battery states – preserving readiness and extending runtime.

If your organization explores optimizing ML on edge devices for performance and battery life, let’s connect. Our engineers specialize in making the most out of power management on edge devices.

Strategy 4: Platform Choices & Optimization

The hardware and software platform you choose shapes not only how efficiently the system runs, but also how your teams build, test, and evolve it over time.

Power-Efficient Architecture for Edge AI Devices

The embedded device market has largely consolidated around ARM-based processors due to their strong focus on performance-per-watt. Technologies like ARM big.LITTLE, which combines high-performance "big" cores with high-efficiency "LITTLE" cores, evolved in response to the need for power-optimized workload management.

A core can sprint when it must (running ML on edge devices) and rest when it can, optimizing both speed and battery life. Many teams now invest in porting software to ARM64, ensuring applications and frameworks are tuned to fully leverage these efficiency advantages across IoT edge devices and enterprise edge device deployment scale systems.

Porting is More Than Recompiling

True optimization goes deeper than recompiling code for a new architecture. Real gains come from rethinking how software interacts with hardware. Porting involves tackling compatibility issues, restructuring build pipelines, and tuning every layer of the stack for efficiency.

At Janea Systems, we specialize in this kind of deep engineering — helping organizations bring complex frameworks to new, more efficient architecture. Our experience in porting software to ARM64 has enabled enterprises to extend the life and performance of their products.

A standout example is porting PyTorch to Windows — a project that brought one of the world’s leading AI tools into the realm of edge AI devices. Our engineers had to:

• Resolve complex compatibility challenges between the framework and the new ARM64 architecture.
• Redesign the CI/CD pipeline to build, test, and validate across diverse hardware targets.
• Fine-tune resource allocation and memory management for consistent performance across a variety of edge computing devices.

This type of foundational engineering enables an entire ecosystem of developers to build and deploy ML models on edge devices. However, architectural choices like this are strategic commitments. Moving to ARM64 or another specialized platform requires parallel investments in compilers, debuggers, testing environments, and talent.

If your organization is exploring a transition to ARM64 or optimizing existing frameworks for edge computing devices, our engineering team can help. Contact us to learn more.

Engineering Sustainable Foundations for Edge AI Devices

True efficiency in edge AI devices isn’t achieved through a single optimization or a last-minute fix. It’s the product of a multi-layered engineering approach, one that touches every phase of design and development, including:

• Hardware–software co-design
• Optimizing ML workloads through right-sizing
• Dynamic system management
• Power-efficient platform architecture

Applying these strategies ensures edge devices remain reliable, responsive, and available. Our experience proves these strategies work.

We partnered with OtoNexus to engineer the Novoscope, a handheld medical edge device. By fine-tuning its power management and optimizing embedded data processing, we helped transform an early prototype into a production-ready clinical solution.

Our team led the ARM64 porting of PyTorch on Windows. This project extended the framework’s reach across healthcare, geospatial, and fintech applications, empowering developers to build and deploy ML models on edge devices with greater flexibility and energy efficiency.

If your organization is working on edge AI devices, our engineering team has expertise to share. Reach out to us to learn how we can help.