Edge AI means running a trained model on the device, or on a gateway next to it, instead of sending raw data to the cloud. It is not always the right answer. This article covers where it pays off, where it does not, and what to settle before a project starts, with attention to Indian field conditions. Why run AI at the edge Latency. A decision made on the device does not wait for a network round trip. Bandwidth and cost. Sending a summary or an alert is far cheaper than strea

Here's the rule: never commit hardware silicon to a product before the accelerator's SDK has actually compiled and run your specific model, on your specific target board, under your specific licensing terms. Every one of those three words — specific — is where teams get burned, because a vendor's demo running their reference model on their reference board under a demo license tells you almost nothing about whether your model, your board, and your commercial deployment will wo

A commercial edge AI accelerator advertising 200+ TOPS at a few watts looks, on paper, like an obvious win for any spacecraft team wanting onboard inference — run a vision model on a satellite instead of downlinking raw imagery for ground processing. The paper number and the flight-qualified reality are two very different things, and the gap between them is where most of the real engineering work in this niche actually lives. Here's what space-grade qualification for this cla

A product spec calls for eight camera feeds running object detection simultaneously off a single edge box. The accelerator vendor's marketing page claims their chip handles dozens of streams. The honest answer to how many streams a real deployment can run is neither the vendor's headline number nor a simple division of TOPS by per-stream compute cost — it depends on model choice, resolution, and a handful of system-level bottlenecks that never show up in a spec-sheet comparis

The Model Evaluated at 94%. The Deployment Gave 61%. What Happened? The evaluation was thorough. The dataset was representative. The model trained to 94% accuracy on the test split. The confusion matrix looked good. The edge device was deployed with confidence. Three months later, the operations team reported that the system was flagging false positives at a rate that made the alerts meaningless. When the engineering team pulled the raw inference logs, they found that the mod

The Demo Ran. The Battery Did Not Survive the Week. Running YOLOv8n on an ESP32-S3 is genuinely impressive. The demo video looks great. Object detection at the edge, no cloud dependency, 5-second inference on a microcontroller — the kind of result that gets excited reactions at conferences and in investor meetings. Then someone asks the power budget question. The ESP32-S3 in inference mode with the camera active draws approximately 250 to 350 milliamperes at 3.3V — roughly 90

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: 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

For a long time, artificial intelligence in engineering conversations has been synonymous with hardware acceleration. GPUs, TPUs, NPUs, CUDA cores, TOPS numbers, benchmark charts. The mental picture is always the same: racks of silicon doing heavy computation, impressive throughput, impressive demos. And then someone asks a much quieter question. “How does this run on a device that costs ₹1,500, runs on a battery, wakes a few times an hour, and is expected to live in the fie

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

When you first encounter LoRaWAN, it feels almost magical. You place a tiny device in your hand, powered maybe by a coin cell, and it whispers a few bytes across a vast distance — sometimes kilometres — landing neatly in a gateway as if by telepathy. For someone who grew up in the world of Wi-Fi and 4G, the idea that a packet can travel so far with so little energy almost feels wrong. But the magic ends very quickly when you connect a real sensor to that device. Because senso

In today’s era of smart infrastructure solutions and IoT product engineering, wearable devices play a pivotal role in embedded systems development and digital transformation for infrastructure. From smart health monitoring to industrial IoT applications, wearables equipped with advanced 9 Degrees of Freedom (9DOF) sensors like the MPU9250 enable precise motion and orientation tracking. However, the common approach of uploading such sensor data to the cloud for processing intr

Introduction In the ever-evolving landscape of smart infrastructure solutions and IoT product engineering, warehouses are rapidly transforming into intelligent, connected ecosystems. Efficient and accurate order fulfilment remains a cornerstone of success in logistics, and innovative technologies like Bluetooth-based tags are now driving this digital shift. By integrating embedded systems development, Edge AI embedded devices, and Industrial IoT and automation, modern warehou

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the Internet of Things (IoT) by enabling real-time data processing directly on edge devices. Traditional IoT systems often depend on centralized cloud servers for computation, which introduces latency and dependency on network connectivity. However, AI-powered embedded systems and Edge AI in embedded devices are changing that — allowing data to be processed locally, securely, and instantly. This innov