Deploying Deterministic TinyML on Constrained Physiological Silicon
“Running machine learning models on cloud servers is trivial; executing low-latency temporal inference on a battery-powered ARM Cortex-M4 microcontroller without overflowing strict heap allocations is a rigorous engineering challenge.”
Memory Budgeting in Biomedical Edge Nodes
In wearable biomedical instrumentation, memory fragmentation is catastrophic. A hard fault due to an unhandled heap allocation during an active patient event invalidates the entire safety envelope.
Our edge architecture employs 8-bit integer quantization (INT8) combined with static tensor arena allocation. By compiling model weights directly into flash memory and pre-allocating deterministic buffer pools, we guarantee constant execution time and zero heap churn.
Feature Extraction vs. Raw Waveform Ingestion
Rather than feeding raw high-frequency waveforms directly into heavy convolutional backbones, we execute domain-informed feature extraction in digital signal processing (DSP) hardware blocks prior to inference.
Extracted frequency-domain energy bands, waveform skewness, and derivative zero-crossing rates reduce input dimensionality by over 90% while retaining critical diagnostic features.
Discover our Applied R&D Labs, Signal Processing & TinyML Pipelines.