MCU → NPU
Classification
Identify events or patterns (OK/Fault, occupancy, appliance type).
SVM • 1D-CNN • TinyMLP
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Edge AI Models and Algos
Edge AI Models & Algorithms
The Brains Behind Every Smart Device.
Every intelligent edge product — from a vehicle ECU to an energy meter — thinks because of its model. We design and deploy optimized AI models that bring perception, prediction, and decision-making into your embedded hardware. Fast, efficient, and secure.
How AI Thinks at the Edge
Cloud AI is built for scale. Edge AI is built for speed and autonomy. Instead of shipping data to remote servers, trained models run inside MCUs, MPUs and NPUs — analyzing sensor signals and acting in milliseconds with full privacy.
Cloud-first
High latency, internet dependency, and bandwidth cost. Best for batch analytics and massive training.
Edge-first
Millisecond decisions, private by design, resilient offline. Ideal for real-time control and safety.
Core Edge AI Model Categories
Choose the right brain for the job. Six families power most embedded products.
Vision / Events
Detection & Localization
Find multiple objects/events (ADAS perception, inspection lines).
YOLO-Nano • MobileNet-SSD
Unsupervised
Anomaly Detection
Spot deviations without predefined labels (motors, CAN, tamper).
Autoencoder • One-Class SVM
Time-series
Forecasting & Predictive
Predict future signals (load, RUL, temperature) with small windows.
ARIMA • LSTM • TCN
Multi-sensor
Sensor Fusion
Combine sensors for robust context (camera+radar, PIR+CO₂).
Kalman • CNN-LSTM Fusion
Closed-loop
Optimization & Control
Continuously tune behavior (HVAC, irrigation, robotics, EV powertrain).
RL • MPC • Adaptive PID
Algorithm Foundations
We combine classical ML, deep learning, and DSP so models are accurate and deployable on constrained hardware.
Signal
Raw Sensor
Vibration • Current • Camera • IMU
Features
DSP Extract
FFT • MFCC • RMS • Kurtosis
Model
ML/DL Inference
SVM • CNN • LSTM • AE
Action
Decide & Act
Relay • CAN Tx • MQTT • UI
Embedded Optimization
Hardware-aware techniques ensure models run in real time with minimal power.
Quantization
INT8/INT16 fixed-point inference with TFLM, CMSIS-NN, eIQ, TIDL, TensorRT.
Pruning & Fusion
Remove redundancy, fuse ops for cache/memory friendliness.
Memory & Scheduling
Arena tiling, zero-copy DMA paths, RTOS task graphs for determinism.
Model → Industry Mapping
Examples of how model families power real products.
| Industry | Use Case | Model Type | Runtime Stack |
|---|---|---|---|
| Automotive | CAN anomaly detection | Autoencoder / One-Class SVM | NXP S32K3 + eIQ |
| Energy | NILM (appliance ID) | 1D CNN / Random Forest | STM32H7 + Cube.AI |
| Industrial | Motor health analysis | FFT + MLP / Autoencoder | STM32H7 + CMSIS-NN |
| HVAC | Occupancy optimization | LSTM / Forecast + Control | i.MX93 + FreeRTOS |
| ADAS / Vision | Object detection | Tiny YOLO / EfficientDet-Lite | Jetson Orin + TensorRT |
Our Approach to Model Engineering
1) Understand the Signal — sampling, noise, physics, and operating envelopes.
2) Design the Model — architecture chosen for latency, memory, and accuracy targets.
3) Train & Quantize — real datasets, calibration sets, INT8/INT16 pipelines.
4) Deploy & Profile — measure accuracy, RAM/Flash, inference time on target hardware.
5) Secure & Scale — encrypted models, secure boot, OTA with rollback and telemetry.
Bring Intelligence to Your Hardware
Need a TinyML classifier on an MCU or a vision detector on an NPU? We’ll design and deploy the right model for your device, data, and product goals.