TinyML & On-Device Microcontroller AI

Microsecond AI Inference Under 256KB RAM

Run sophisticated machine learning models directly on low-power ARM Cortex-M microcontrollers and DSPs. Eliminate cloud latency, cellular bandwidth costs, and privacy vulnerabilities by processing sensor signals at the extreme edge.

Scope Your TinyML Model
TinyML edge AI anomaly detection on industrial motor vibration data

Why Cloud AI Fails on Battery Hardware

Streaming raw high-frequency sensor streams (such as 10kHz vibration or audio) to the cloud exhausts cellular bandwidth, drains lithium batteries in hours, and introduces unacceptable latency for emergency shutdowns. TinyML executes inference in microseconds locally on the microcontroller.

  • Post-Training INT8 Quantization: Squeeze heavy neural networks down to 8-bit integer math, shrinking model size by 75% with zero perceptible loss in accuracy.
  • CMSIS-NN & Hardware DSP Acceleration: Leverage ARM Cortex-M SIMD instructions and DSP extensions to accelerate matrix multiplications by up to 5x.
  • Milliwatt Power Envelopes: Wake-on-sound or wake-on-vibration models that draw microamps until an anomaly threshold is triggered.
  • 100% Data Privacy & Air-Gapped Operation: Sensor signals are classified in volatile MCU RAM and never leave the device.
TinyML Capabilities

Our Embedded AI Services

Vibration Anomaly Detection

Continuous FFT and spectral feature extraction on 3-axis/6-axis accelerometer feeds to predict bearing failure and motor misalignment before downtime occurs.

Acoustic & Sound Classification

Deploy ultra-compact 1D CNNs for glass break detection, industrial machine sound profiling, audio keyword spotting, and voice biometric authentication.

Model Optimization & MCU Porting

Translate complex PyTorch/TensorFlow models into C++ header arrays using TensorFlow Lite for Microcontrollers (TFLite Micro) and custom C kernels.

Extreme Edge Inference Pipeline

1. On-Device Digital Signal Processing (DSP)

Raw time-series data is preprocessed on the MCU using Fast Fourier Transforms (FFT), Mel-frequency cepstral coefficients (MFCC), and low-pass filtering before feeding the neural network.

2. Tensor Arena Memory Profiling

We precisely calculate tensor arena buffers down to the exact byte, ensuring activation memory fits comfortably in available SRAM alongside the FreeRTOS TCP/IP stack.

3. Hardware Validation Across Silicon

We benchmark and profile latency, power consumption, and thermal footprint directly on target hardware (STM32H7, NXP RT1060, Nordic nRF5340, ESP32-S3).

Embedded Intelligence Matrix

TinyML Microcontrollers vs. Embedded Linux vs. Cloud AI

Comparing execution models for intelligent edge sensing across silicon tiers, power envelopes, and latency constraints.

Performance Dimension TinyML on MCUs (Cortex-M / ESP32) Embedded Linux (Pi / i.MX8) Cloud Inference Pipelines
Power Consumption 10µW - 50mW (Multi-year battery lifespan) 2W - 15W (Requires active heatsink & DC supply) High modem draw (Continuous cellular/Wi-Fi uplink)
Inference Latency 2ms - 30ms hard real-time deterministic 50ms - 200ms (Linux kernel & scheduler jitter) 200ms - 3000ms (Subject to WAN latency & jitter)
Hardware BOM Cost \$1.50 - \$8.00 per chip unit \$35.00 - \$150.00 per compute SOM Low hardware cost + recurring monthly cloud & SIM billing
Data Privacy & Air-Gap 100% localized; raw telemetry never leaves silicon Local processing; exposed to OS-level CVEs Data in transit across public internet; GDPR/HIPAA exposure
Memory Envelope 64KB - 1MB SRAM, 512KB - 2MB Flash 512MB - 8GB LPDDR RAM Elastic multi-gigabyte server memory
Target Workloads Acoustic vibration anomaly, wake-word, IMU motion, Person Detect Multi-stream video decoding, local NVR analytics, complex robotics Multimodal LLMs, massive cohort training, global business logic

Deploy Intelligence to the Extreme Edge

Transform your embedded sensors into intelligent, autonomous edge nodes. Partner with TinyML engineering specialists.

Discuss Your TinyML Pipeline
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Book a zero-pitch, 20-minute working session to review your MCU RAM/Flash budgets, audit DSP feature extraction code, or evaluate INT8 quantization accuracy on your sensor datasets.

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Discuss your architecture, feasibility, hardware sizing, or custom software requirements directly with a senior engineer.

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