The Hook: Stepping Out of the Codebase You’ve mastered C/C++. You can write interrupt service routines in your sleep, and you know exactly how to squeeze that last byte of RAM out of an ARM Cortex-M4. When a hard fault occurs, you dive into the disassembly and register dumps with the confidence of a seasoned detective. You are a highly effective firmware developer. But lately, you’ve noticed a shift. The challenges that excite you aren't just about optimizing a single driver

Edge AI is moving inference down to battery-powered sensors, appliances and small edge nodes. That makes the processor architecture a real design decision, because it affects power, cost, software effort and supply options for years. ARM Cortex-M cores have long been the default in embedded systems. RISC-V, an open instruction set with a modular design, is now a serious alternative for new silicon. This article compares the two for AI workloads: their licensing models, how ea

Reading Time: 25 mins Topic: Compiler Engineering & Optimization Bridging the gap between academic projects and industry reality. Compiler Toolchain Concept The Hook: Why We Are Rethinking the Compiler in 2026 For decades, if you were building firmware for an embedded C project, your compiler choice was made for you: GCC (GNU Compiler Collection). It was the unquestionable industry standard. ARM shipped `arm-none-eabi-gcc`. The open-source community rallied behind it. It work

Reading Time: 25 mins Topic: RTOS Selection & AI Scheduling Bridging the gap between academic projects and industry reality. Visualizing Circuit board with glowing ai chip. Photo via Unsplash. The Hook: When Determinism Meets the Black Box Imagine a drone navigating a dense forest at 50 km/h. Its flight controller needs hard real-time guarantees—motor updates every 1 millisecond, with sub-microsecond jitter. Simultaneously, a neural network is processing 30fps video feeds to

Author: Srihari Maddula Reading Time: 25 mins Topic: Edge AI & Neural Acceleration Intelligence at the edge isn't about power—it's about the architecture of the silicon. Photo via Unsplash. In the world of mainstream AI, NVIDIA is king. Students and hobbyists are taught that if you want to do Deep Learning or Computer Vision, you need a high-end GPU and the CUDA software stack. This mindset leads to a common design failure in the embedded world: trying to strap a 30-watt Jets

Ancient history or hidden powerhouse? The truth about the 8051. (Photo by Alexandre Debiève on Unsplash) The College Dilemma: "Why Are We Learning This Fossil?" It’s a rite of passage for every electronics engineering student in India (and many parts of the world). You walk into your Microprocessors lab, and there it is: The 8051 Development Board. It looks like it belongs in a museum. You spend weeks memorizing assembly instructions like MOV A, #05H and DJNZ. You wrestle wit

A EurthTech Deep Technical Perspective If you’ve ever tried running AI on a microcontroller, you probably remember two contrasting moments. The first moment is magical — the instant you see your tiny model running on a bare-metal MCU, making sense of a sensor stream. It feels like science fiction. A kilobyte-sized network doing real work, right at the edge. And the second moment happens soon after — when the magic suddenly collapses under noise, jitter, power fluctuations, ra

By [Employee Name]/[Designation] at EurthTech Published on July 10, 2025 The Real-World Problem Let’s say you're developing a smart fitness band—an example of modern embedded systems development and IoT product engineering. It needs to: Continuously read accelerometer + heart rate data Push updates via BLE Display information on an OLED Stay alive for a week And run TinyML gesture detection using Edge AI in embedded devices All on something like an Apollo4 Blue or STM32L4. H

For decades, creating a custom FPGA or ASIC was something only multinational semiconductor giants could afford.EDA tools were proprietary. Foundry access was restricted. And costs were astronomical. Startups, research labs, or embedded engineering teams didn’t “design silicon.”They bought off-the-shelf chips and optimized around them. But the wall that separated innovators from silicon has now cracked wide open. A new generation of open-source FPGA and ASIC toolchains has mad

A phone call.A drone video feed.A satellite downlink.A Wi-Fi packet.A radar sweep. They all seem simple on the surface — but under every one of these technologies lies the same invisible foundation:Digital Signal Processing (DSP). DSP is the mathematics that turns radio waves into information.It’s the invisible layer that powers nearly every communication system — from smartphones to satellites. Two decades ago, this was the territory of defense labs and chip manufacturers.To