Guides
Edge AI & TinyML engineering guides
Practical, in-depth guides on putting AI on devices — TinyML fundamentals, model compression, sensor fusion, deployment, and real-world applications across industries.
What Is TinyML? A Complete Guide to Machine Learning on Microcontrollers
TinyML brings neural networks to microcontrollers running on milliwatts. Learn what TinyML is, how it works, what hardware it runs on, and where it beats cloud AI.
Edge AI vs Cloud AI: When On-Device Inference Actually Wins
Latency, privacy, offline operation, and cost — a clear-eyed comparison of edge AI and cloud AI for embedded products, and how to decide which your device needs.
AI on Microcontrollers: How Neural Networks Run on Cortex-M and RISC-V
Inside MCU machine learning: memory constraints, int8 arithmetic, inference runtimes like TFLM and CMSIS-NN, vendor NPUs, and what kinds of models actually fit.
Is Your Product a Fit for TinyML? A Feasibility Audit Checklist
Before you budget an embedded AI project, run the feasibility audit: data quality, model size, power envelope, sensor bandwidth, and the honest 'maybe not' cases.
From Sensor Data to Decisions: How an Edge AI Pipeline Actually Works
The anatomy of an embedded AI system: sampling, filtering, feature extraction, inference, and post-processing — and why the model is the smallest part.
Model Compression for Edge AI: Quantization, Pruning & Distillation
The three compression techniques that shrink neural networks from megabytes to kilobytes — quantization, pruning, and knowledge distillation — and how to combine them.
INT8 Quantization for Embedded AI: A Practical Guide
How int8 quantization works under the hood — scales, zero-points, QAT vs PTQ, per-channel vs per-tensor — and how to debug accuracy loss on the target.
Getting Started with TensorFlow Lite Micro for Embedded AI
The standard TinyML runtime end to end: train, convert to TFLite, quantize, compile to C, and integrate TFLM into firmware on Cortex-M and RISC-V.
Sensor Fusion on Embedded Devices: Combining IMU, Audio & Environmental Data
How to fuse multiple sensor streams on-device — IMU, acoustic, current, environmental — into decisions a single sensor can't make, from Kalman filters to ML classifiers.
On-Device Anomaly Detection: Catching Rare Events with Sensor Data
Anomaly detection on microcontrollers — autoencoders, statistical baselines, and one-class models that flag the weird without needing labeled failure data.
Wake Word Detection on Microcontrollers: Always-On Audio at Microwatts
How keyword spotting works on MCUs: MFCC features, small CNN models, always-on duty cycles, false-wake tuning, and keeping the whole pipeline under a milliwatt.
Gesture & Activity Recognition from IMU Data on Microcontrollers
Turning accelerometer and gyroscope data into gestures and activities: windowing, features, model architectures, and the classic pitfalls of IMU-based ML.
Power Budgeting for Battery-Powered AI Devices
Making AI run for years on a battery: duty cycling, sleep states, staged inference, energy harvesting, and the per-inference energy math that shapes architecture.
OTA Model Updates & Fleet Management for Edge AI
Shipping improved models to devices in the field: versioned model payloads, staged rollouts, health checks, rollback, and the field-data flywheel.
Predictive Maintenance with Edge AI: Catching Machine Faults on the Factory Floor
How embedded AI monitors motors, bearings, and rotating equipment — vibration and current sensing, anomaly detection at the node, and no cloud required on the floor.
Edge AI for Industrial IoT & Energy: Metering, Monitoring, and Fault Detection
On-node intelligence for industrial IoT: smart metering, load disaggregation, grid monitoring, and asset health across oil, gas, grid, and utilities.
Edge AI in Smart Pet Devices: Activity, Health & Behavior Monitoring
How TinyML powers pet wearables and feeders — activity recognition on collars, eating/health pattern monitoring, anomaly alerts, and months of battery on a coin cell.
Edge AI in Smart Home Devices: Presence, Voice & Privacy-First Sensing
Local intelligence for smart home products — on-device wake words, presence sensing, audio event detection, and why privacy-first architectures win in the home.
AI on Agricultural & Environmental Sensor Nodes
Solar- and battery-powered field nodes that classify events on-device: pest detection, irrigation monitoring, livestock behavior, and store-and-forward telemetry over LPWAN.
On-Device Biosignal AI: Health & Fitness Monitoring on Wearables
ECG, PPG, and motion analysis on the wrist and body — on-device arrhythmia detection, activity classification, sleep staging, and privacy-first biosensing.
Edge AI in Automotive & Mobility: In-Cabin Sensing and Road-Event Detection
On-device intelligence for vehicles: driver monitoring, occupancy detection, road-event classification on ECUs and telematics — where latency is safety.
Edge AI for Consumer Devices: Voice, Gesture & Context on a Coin Cell
What actually runs on-device in consumer products: wake words, gesture controls, context awareness for wearables, hearables, and smart home — at power budgets that fit a coin cell.