In the world of embedded systems, we have long operated under the mantra of “doing more with less.” Traditionally, this was a battle for survival; cramming a complex RTOS and a networking stack into 64KB of Flash was a badge of honor, and shaving microamps off a sleep cycle was the difference between a product that lasted a year and one that died in a week.
However, the stakes have shifted. As we move into 2026, the “constrained” in constrained coding is no longer just about hardware limitations or battery life. It is about the global carbon footprint. With billions of microcontrollers (MCUs) deployed annually, the micro-joules we save in a single loop iteration scale into megawatt-hours across the global fleet.
For the modern embedded engineer, the compiler is no longer just a tool to translate C into machine code; it is an environmental lever. This article provides an in-depth analysis of how code size, memory architecture, and compiler optimization levels (O0 through Os) dictate the carbon cost of our silicon.
1. The Physics of Power: Where the Joules Go
To understand the energy impact of code, we must first look at the underlying physics of the CMOS (Complementary Metal-Oxide-Semiconductor) circuits that power our MCUs. The power consumption of a digital circuit is generally defined by the sum of static and dynamic power:
Ptotal=Pstatic+Pdynamic
Where dynamic power is the primary driver during code execution, governed by:
Pdynamic=α⋅C⋅Vdd2⋅f
- α: The switching activity factor (how many gates flip per clock cycle).
- C: The capacitive load.
- Vdd: The supply voltage.
- f: The operating frequency.
The Hidden Cost of the Instruction Fetch
Every time your CPU executes an instruction, it must first fetch it from memory. In an embedded context, this usually means a trip to the On-chip Flash.
Flash memory is notoriously “expensive” in terms of energy. Fetching a 32-bit word from Flash can consume significantly more energy than executing the instruction itself within the CPU core. Why? Because Flash requires higher voltages for sense amplifiers and has higher parasitic capacitance than the CPU’s internal registers or L1 cache.
When code size increases, the likelihood of Instruction Cache (I-Cache) misses rises. A miss forces the system to stall while it fetches from the “slower, hungrier” Flash memory. Consequently, a bloated binary doesn’t just waste space; it forces the hardware to work harder for every single operation performed.
2. The Compiler’s Dilemma: Speed vs. Size
Embedded compilers (GCC, LLVM, IAR) offer various optimization levels. Each is a collection of “passes” that rearrange your code to meet a specific goal. But these goals often have conflicting energy profiles.
-O0: The Carbon Catastrophe
Optimization level zero is designed for debugging. It maps source code directly to assembly with minimal transformation. The result is “honest” code that is easy to step through, but it is also bloated and riddled with unnecessary stack operations.
- Energy Impact: High. The CPU spends more cycles moving data to and from the stack, increasing the t (time) in the E=P⋅t equation without a corresponding decrease in P.
-O2 and -O3: The “Race to Sleep” Champions
These levels prioritize execution speed. They employ aggressive techniques like Loop Unrolling and Function Inlining.
- Loop Unrolling: Replaces a loop with repeated instances of the loop body. This reduces the branch overhead (fewer CMP and BNE instructions).
- Function Inlining: Replaces a function call with the actual code of the function, eliminating the overhead of pushing/popping registers from the stack.
The Trade-off: While these optimizations make the code run faster (reducing t), they significantly increase the Code Size. If the unrolled loop becomes too large to fit in the I-Cache, you encounter a “Performance Wall.” The power consumption (P) spikes because the Instruction Management Unit (IMU) is constantly hammering the Flash memory.
-Os and -Oz: The Density Saviors
-Os (Optimize for Size) and the even more aggressive -Oz prioritize code density. They look for common instruction sequences and replace them with subroutines or use more compact instruction sets (like ARM’s Thumb-2).
- Energy Impact: Lower Pdynamic via higher cache hit rates. By keeping the working set of instructions small, the CPU can stay within the low-power I-Cache longer, avoiding the power-hungry Flash bus.
3. Benchmarking the Impact: Speed vs. Sustainability
Recent research (including studies from 2025) suggests that for many “event-driven” IoT applications, the most efficient optimization isn’t always the fastest.
Let’s look at a comparative table of how these levels typically affect a standard ARM Cortex-M4 based sensor node:
| Optimization Level | Code Size | Execution Time | Avg. Power (Active) | Energy per Task (mJ) |
| -O0 | 100% (Base) | 100% (Base) | 10.2 mW | 1.00 mJ |
| -O2 | 75% | 40% | 12.5 mW | 0.50 mJ |
| -O3 | 120% | 35% | 14.8 mW | 0.52 mJ |
| -Os | 65% | 50% | 10.8 mW | 0.54 mJ |
The Observation: Notice that while -O3 is the fastest, it actually consumes more energy per task than -O2 in this scenario. This is often due to “cache thrashing” caused by excessive loop unrolling. The extra power required to manage the larger code footprint outweighs the time saved.
4. The “Race to Sleep” Strategy and the Duty Cycle
In embedded engineering, we often talk about the “Race to Sleep.” The idea is simple: run the CPU at maximum speed to finish the task as quickly as possible, then immediately drop into a deep-sleep state (μA range).
Etotal=(Pactive⋅tactive)+(Psleep⋅tsleep)
If your application has a low duty cycle (e.g., waking up once every 10 minutes to read a sensor), the “Race to Sleep” via -O2 or -O3 is usually the greenest choice. However, as the duty cycle increases (e.g., real-time audio processing or high-frequency vibration analysis), the Active Power (Pactive) becomes the dominant factor. In these cases, the code density of -Os becomes superior because it minimizes the “switching cost” of instruction fetching.
5. Memory Architecture: The Silent Carbon Contributor
The layout of your code in memory is just as important as the instructions themselves. Modern MCUs are increasingly using multi-banked Flash and SRAM.
Flash Page Activations
Flash memory is organized into pages (e.g., 2KB or 4KB). To read an instruction, the page must be “opened” or “powered up.” If your frequently accessed code (an ISR or a hot loop) straddles the boundary of two Flash pages, the MCU must keep both pages powered.
- The Constrained Coding Fix: Use linker scripts to align “hot” sections of code to single Flash pages. This reduces the static leakage of unused Flash sections.
External Memory: The Energy Abyss
If your code size exceeds the internal Flash and spills over into an external QSPI Flash or SDRAM, the energy cost per instruction fetch can increase by 10x to 100x.
- The Carbon Cost: Every bit toggling across a PCB trace has a capacitive cost. Compressing code via -Os to fit entirely within the internal memory is perhaps the single most effective “green” optimization an embedded engineer can perform.
6. The Ethical Imperative for Embedded Engineers
As of early 2026, it is estimated that digital technologies contribute to nearly 4% of global greenhouse gas emissions. While a single smart toaster seems insignificant, the “Grey Fog” of trillions of active instructions across the planet is not.
We have a professional responsibility to treat Energy as a First-Class Constraint, alongside RAM and Flash. This means:
- Stop using -O0 in production: It’s not just lazy; it’s environmentally irresponsible.
- Profile for Energy, not just Speed: Use power debuggers to see the actual Joules consumed by your code, not just the cycle count.
- Optimize the “Hot Path”: Don’t optimize the whole binary for speed. Use __attribute__((optimize(“O3”))) for math-heavy loops and -Os for the rest of the system to keep the overall footprint small.
7. Beyond the Compiler: Language Choice and Data Widths
While C remains the king of the “green” hill due to its proximity to hardware, the way we handle data within C also matters.
Data Width Optimization
Using a 32-bit int to store a value that fits in an 8-bit uint8_t isn’t just a waste of RAM. On many 8-bit or 16-bit architectures (still prevalent in ultra-low-power sensing), processing a 32-bit variable requires multiple clock cycles and multiple instruction fetches.
- Recent Breakthroughs: New bit-level analysis in modern compilers (LLVM-based) can now automatically “downsize” variables if they don’t exceed a certain range, but manual diligence is still faster and more reliable.
The Cost of Abstraction
C++ abstractions (like std::vector or heavy template metaprogramming) can lead to Code Bloat. While templates are “zero-cost” in terms of runtime speed, they are often “high-cost” in terms of code size. Multiple instantiations of a template for different types result in multiple copies of the code in Flash.
- Strategy: Be mindful of template specialization. Use “thin wrappers” or base classes to share code between different template instances.
8. Conclusion: The Future is Dense and Fast
The “Carbon Cost of Constrained Coding” is a multifaceted problem. It requires us to look past the simple binary of “size vs. speed” and look at the holistic energy profile of the system.
By choosing -Os when cache hits matter, -O2 when the “Race to Sleep” is viable, and meticulously aligning our code to memory boundaries, we do more than just build better products. We reduce the invisible carbon toll of the digital world.
The tools are already in our hands. Compilers are smarter than ever, and power-profiling hardware is now affordable for every desk. The only thing missing is a shift in mindset: seeing every byte of code not just as a piece of logic, but as a commitment of energy.
Ready to Optimize Your System’s Footprint?
At RunTime, we believe that the best code is code that respects its environment—both the silicon and the planet. Whether you are struggling to fit a neural network into a Cortex-M0 or trying to extend the battery life of a global IoT fleet, we are here to help.
Connect with RunTime today to join a community of engineers dedicated to high-performance, low-energy embedded design. Let’s build a future where our code leaves a mark on the world—but not on the climate.
