Edge AI & Vision

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

A EurthTech Deep Technical Narrative If you’ve ever been part of a product team deciding “what hardware should we run our AI model on,” you already know the feeling.The whiteboard fills up quickly: ESP32 here, STM32 there, a Raspberry Pi scribbled in the corner, someone writes “Jetson Nano?” and someone else writes “Coral TPU?” with a question mark the size of an existential crisis. You stare at the options and the room feels divided between two tribes:the “microcontroller mi

Seeing Is Easy. Understanding Is Expensive. Why Edge Vision and Geospatial Reasoning Matter More Than Ever in the AI Era We are living in an era where “vision AI” no longer needs an introduction. Everyone has seen demos of cameras recognising objects, faces, vehicles, defects, crops, or people. Large models can describe scenes in natural language. LLMs can reason about what an image might contain and generate insights that feel almost human. From the outside, it looks as if v

When Small Cameras Change Big Logistics Decisions How Edge Vision and Geospatial Intelligence Are Quietly Replacing Expensive Asset-Tracking Systems Logistics organisations have spent the last decade chasing visibility. They have invested in RFID infrastructure, proprietary RTLS systems, high-end industrial cameras, cloud-heavy analytics platforms, and manual reconciliation processes. The promise was clear: if assets could be tracked precisely, delays would reduce, losses wou

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

Ask a general-purpose large language model to diagnose the likely cause of a specific bearing failure signature, and it will produce a confident, well-written, plausible-sounding answer drawn from the statistical patterns in its training data. Ask it the same question again with slightly different phrasing, and it may produce a different, equally confident answer. Neither answer comes with a traceable path back to a specific dataset, a specific prior failure record, or a spec

The pipeline works. Species detection is solid, individual tracking holds across occlusion, a perimeter-crossing alert fires within a second of a real event, and a live dashboard shows counts by zone in real time. Then someone asks what license the detection model ships under, and the honest answer for most teams building on the obvious starting point — the YOLO family — is a license that makes the product, as currently built, unshippable as a closed commercial system without

A cluster reports 40% average GPU utilization. Finance sees the invoice and asks why. The platform team points at the scheduler and says it did its job — every pod that asked for a GPU got one. Both are right, and both are describing a system that is architecturally blind to the thing that actually matters: what a workload does with the GPU once it has it. This is the gap between resource allocation and resource placement. Kubernetes-native schedulers were built for a world w

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

Why on-device AI matters For decades, firmware meant deterministic control: read sensors, drive outputs, manage power within tight limits, and leave the heavy thinking to the cloud. That is changing. Compressed neural networks now run on the microcontrollers that sit next to the sensors. So can a microcontroller run a large language model? Not in the way a cloud LLM runs. Small, specialized models do run well on MCUs today, and combining them is where the useful products are.

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

Why Edge Vision and Geospatial Intelligence Are Redefining Large-Scale Operations Ports and large logistics yards are often described as data-rich environments. There are cameras everywhere.Sensors on cranes, vehicles, and containers.GPS feeds from trucks and vessels.Dashboards tracking throughput, dwell time, and utilisation. From the outside, it appears that visibility is already solved. Inside these operations, however, decision-makers know a different reality. Despite eno

Why Smart Retail Heatmapping Needs Spatial Intelligence, Not Surveillance Retail has always been a spatial business. Revenue depends on how people move through space.Costs depend on how space is allocated.Experience depends on how congestion forms and dissolves. Long before analytics platforms existed, store managers understood this intuitively. They watched foot traffic, rearranged aisles, repositioned counters, and learned where customers lingered or avoided. Decisions were

Why Geo-Enabled Vision Systems Are Becoming the Backbone of Construction Site Reality Construction sites are among the most dynamic environments humans operate in. Layouts change daily. Crews rotate constantly. Equipment moves unpredictably. Plans evolve under real-world constraints. And yet, most safety and progress monitoring systems still assume static conditions punctuated by periodic checks. Inspections happen weekly. Progress is reported manually. Safety compliance is a

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