Embedded Solutions for Improved Performance

Your interrupt handlers might be silently killing your embedded system's performance. ⚠️ After 5+ years optimizing real-time systems, I've watched countless embedded projects fail because developers treated ISRs (Interrupt Service Routines) like regular code. The truth? Interrupt handlers demand a fundamentally different mindset. Here's what separates elite embedded engineers from the rest: ✅ They keep ISRs ruthlessly minimal — acknowledge the interrupt, capture essential data, signal a task, then EXIT immediately ✅ They religiously avoid these ISR performance killers: • Dynamic memory allocation (no new/delete!) • Complex calculations • Heavy C++ features (RTTI, exceptions, streams) • Lengthy loops or blocking operations ✅ They strategically disable interrupts during critical sections but use this power sparingly to minimize system latency ✅ They design proper interrupt priority schemes that match their system's real-time requirements The most successful embedded teams I've worked with follow a simple philosophy: "Do as little as possible inside the ISR, defer everything else." This approach has helped my clients reduce interrupt latency by up to 87% in mission-critical medical devices and industrial automation systems. What techniques do you rely on to keep your interrupt handlers efficient and deterministic? Share your best practices below! #EmbeddedSystems #CPP #Interrupts #RealTime #SoftwareEngineering #EmbeddedC++ #Performance

Most embedded engineers (my past self included) believe that free compilers (like GCC) are “good enough.” They’re free. They’re open-source. They compile your code. What else do you need, right? But here’s the thing nobody tells you: “Good enough” doesn’t always cut it. Especially when performance is non-negotiable. Here’s what I discovered: I ran a deep-dive benchmark comparing GCC vs. IAR Embedded Workbench across multiple RTOS environments: PX5, FreeRTOS, and ThreadX. And the results? IAR outperformed GCC by 20–40% in most cases. Let that sink in. We’re talking about real-world scenarios where every microsecond matters and GCC just can’t keep up. A few surprising insights: 🔹 In tests like Cooperative Scheduling, both compilers were neck and neck. Why? Because it’s mostly assembly, and both optimize that well. 🔹 But in Memory Allocation and Message Processing? Massive gaps. IAR crushed GCC. So what? If you’re building firmware where speed, efficiency, and tight resource usage are critical (think low-power devices, real-time systems, mission-critical apps, this isn’t just a “nice-to-have” insight. It could mean the difference between firmware that runs flawlessly and firmware that lags, drains power, or fails. Here’s what I wish someone had told me earlier: 🔹 Don’t blindly trust your compiler 🔹 Don’t assume open-source is always “optimized enough” 🔹 If performance is king, commercial tools like IAR might be your secret weapon. 🔹 If you want to see the raw numbers, grab the full RTOS Performance Report here: https://lnkd.in/gZDB3Wi5

New Article: Why C++26 is a Game-Changer for Embedded Systems 🚀 After 30+ years in embedded development (from IoT devices to spacecraft), I'm genuinely excited about C++26's upcoming features. These language improvements directly address some of our biggest challenges: memory constraints, power efficiency, and the eternal trade-off between performance and testability. The standout features for our field: + Compile-time reflection for zero-cost dependency injection + Pattern matching that makes state machines cleaner and safer + Static containers with predictable memory footprint + Enhanced constexpr moving computation from runtime to build time These aren't just syntax improvements—they're architectural solutions that let us write maintainable, testable code without sacrificing the performance embedded systems demand. Currently seeking new embedded opportunities and would love to hear your thoughts on how these features might impact your projects.

In embedded systems development, particularly on small MCUs like the ATtiny1616, developers often face significant resource constraints. These devices typically offer no hardware support for multitasking, possess limited RAM (often ~2KB or less), and feature a single-core architecture. To handle multiple time-sensitive tasks—such as reading sensors via ADC, communicating over USART or I2C, and controlling GPIOs—embedded developers must design software that emulates concurrency. The most effective way to accomplish this is through a combination of Cooperative Scheduler and State Machine design patterns. This article explores the use of these patterns together and provides a real-world implementation on the ATtiny1616 platform.

Small Buffer Optimization in C++: Avoiding Heap Allocations for Small Objects Last week, we conducted a poll, and the winning topic was Small Buffer Optimization (SBO). SBO is an internal optimization strategy used by standard containers—like `std::string`—to store small amounts of data directly within the object’s memory footprint, avoiding heap allocations for short inputs. This technique can, under specific conditions, reduce memory overhead and improve performance by eliminating the need for dynamic memory allocation when dealing with small-sized content. However, SBO comes with its own trade-offs. The size of the internal buffer is fixed by the library implementation, so once the content exceeds that limit, the container falls back to dynamic allocation—incurring the usual performance costs. In scenarios where most inputs are short—such as parsing configuration files or handling small tokens—SBO can result in significant performance gains, especially due to better cache locality and the avoidance of allocator pressure. But in projects that frequently deal with large or unpredictable input sizes, SBO offers little advantage, and the fallback to heap allocation becomes the dominant behavior. For instance, in Visual Studio 2022, SBO is enabled by default in the MSVC STL implementation. This serves as a reminder that small, low-level optimizations like SBO are often dependent on the standard library and toolchain. While not directly configurable, their impact is real and measurable—especially in tight loops, embedded contexts, or latency-sensitive code paths. Have you ever profiled your code and found out that SBO was silently improving your performance? Or maybe you switched compilers and noticed behavioral changes? Tell us in the comments—we’re curious to hear about your experience! NOTE: Below is a small example extracted from the _String_val class of the Microsoft STL, illustrating SBO in action. #Performance #Cpp #SmallBufferOptimization #SBO #STL #MSVC #VisualStudio2022 #MemoryOptimization #SoftwareEngineering #CppDev #LowLevelProgramming #HeapAvoidance #ToolchainTips #Cpp26 #StringHandling #OptimizationFlags #CodeTuning #ModernCpp #TechInsights #EngineeringTips

Optimized an ESP32 program for very fast video streaming. The example programs that come with a lot of these devices are good starting points. But for practical applications a lot more intelligence needs to be added in to deal with bandwidth issues and channel issues including co-channel and adjacent channel interference. By default the example program I was using began at 40 MHz bandwidth in access point (AP) mode which created a lot of co-channel and adjacent channel interference. Reducing the bandwidth to 20 MHz helped to allow channel placement within 1, 6 or 11 and not overlap other devices. And finally an algorithm to check the received signal strength (RSSI) of the other devices within range and choose a channel with minimal interference. The video transfer was very fast, nearly real-time which was awesome for such a small and inexpensive device. I think this is the fun and satisfying part of software engineering; to take an existing design/method and to improve on the performance and reliability to make it more useful for a broader range of applications. #engineering #softwareengineering #reliabilityengineering #embeddeddesign