TinyML & Embedded Signal Processing Platform
Deploy artificial intelligence models directly onto microcontrollers and micro-NPUs right next to the physical signal. Detect vibration, acoustic, and thermal anomalies in real time with ultra-low power consumption.
TinyML & Embedded Signal Processing Platform
Drastic reduction in cellular data costs, near-instant local alert reaction, and preserved operational autonomy even during network blackouts.
Streaming continuous raw sensor signals to the cloud saturates 4G cellular links and drives up telecommunication bills. Furthermore, a single network drop prevents real-time diagnostics.
Artificial intelligence analyzes the signal directly on the sensor microcontroller. Only qualified alerts and anomalies are transmitted over the air.
Silicon Integration Bottlenecks & Physical Constraints
Critical failure modes observed when porting AI models onto industrial microcontrollers:
Memory Footprint & SRAM Constraints
Neural network architectures trained in PyTorch routinely exceed internal microcontroller SRAM (256 to 512 KB), causing stack overflows without dedicated tensor refactoring.
High-Frequency DMA Synchronous Sampling
Continuously sampling MEMS vibration sensors at 10 to 20 kHz without jitter while concurrently feeding NPU inference requires strict double-buffered DMA management in low-level C.
Model Recalibration & Field Evolution
Models frozen in static flash ROM cannot adapt to evolving machine wear profiles without a dedicated architecture for secure incremental weights updates over-the-air.
System Architecture: The Edge Pulsar Approach
A complete software pipeline to port and execute diagnostic algorithms on resource-constrained silicon:
Preprocessing & Feature Extraction
Execution of FFTs, wavelet transforms, and spectral descriptors accelerated via DSP instructions (CMSIS-DSP) directly on incoming DMA sensor buffers.
Hardware INT8/INT4 Quantization
Quantization and layer alignment optimized for target accelerators (Arm Ethos, STM32N6 Neural-Art) while strictly preserving diagnostic sensitivity.
Deterministic Local Inference
Closed-loop execution without dynamic memory allocation, guaranteeing sub-millisecond execution latency and zero execution drift.
Secure Incremental Weights FOTA
Dedicated flash partition architecture enabling over-the-air updates of neural weights without modifying the host real-time operating system.
Why Choose Our Architecture Over In-House Development?
The critical factors for securing your microcontroller AI deployments:
Convergence of Data Science & Embedded C
Your teams develop domain models in Python; we ensure optimal porting, quantization, and real-time execution on constrained silicon targets.
Energy Autonomy Preservation
Local inference eliminates continuous radio transmission of heavy raw sensor streams, preserving battery life and lowering connectivity costs.
Long-Term Predictive Performance
Our secure update blocks enable model recalibration to mechanical wear throughout the entire equipment lifecycle without hardware intervention.
Validated Industrial Applications
Predictive Maintenance on Bearings & Pumps
Continuous spectral vibration analysis to detect flaking, misalignment, or cavitation before catastrophic failures.
Acoustic Arc & Fluid Leak Detection
High-frequency acoustic monitoring on pressurized gas/liquid pipes and high-voltage electrical switchgear.
Thermal Anomaly & Motor Drift Supervision
Early hotspot detection on remote industrial machines lacking continuous wired network connectivity.
Related Engineering Services
Tailor, integrate, or maintain this architecture with our dedicated embedded engineering and lifecycle services.
Ultra-Low-Power MCU Board Support Packages
Bare-metal and RTOS driver development, clock tree tuning, and ultra-fast wake-up from low-power modes.
Deterministic RTOS & Task Scheduling
Hard real-time scheduling on Zephyr or FreeRTOS isolating high-frequency sensor acquisition loops.
Edge AI Quantization & Benchmarking
Validating model accuracy, INT8/INT4 quantization loss, and inference latency on physical target silicon.
Validate TinyML feasibility on your physical sensor signals
Qualify your machine learning models on industrial microcontrollers.