The Intelligence Leap:
When AI Lives Inside the Thing
A deep-dive technical presentation and interactive laboratory on running real-time deep learning inference directly on constrained microcontrollers (ESP32-S3, Arduino Nano 33 BLE Sense, ARM Cortex-M) without cloud dependence.
Interactive Presentations & Simulation Labs
Explore the live interactive components built specifically for this conference talk and technical workshop.
Interactive 32-Slide Conference Deck
Complete slide presentation featuring custom canvas scaling, keyboard navigation (Space / Arrows), live animations, and embedded interactive telemetry viewers.
Multi-Sensor Fusion Explorer
Bidirectional simulation testing environmental (temperature/humidity), acoustic, and 6-axis IMU vibration data pipelines with real-time classification graphs.
End-to-End Edge AI Flow Diagram
Interactive architectural flowchart mapping sensor acquisition, DSP feature extraction (MFCC/FFT), TensorFlow Lite Micro quantization, and bare-metal execution.
Arduino Nano 33 & ESP32-S3 Hardware Specs
Microcontroller peripheral anatomy, memory constraints, power states, and pinout breakdown for deployable TinyML sensor edge nodes.
The Four Embedded Constraints of Edge AI
Why running machine learning on microcontrollers requires a fundamentally different engineering mindset than cloud computing.