AI / ML
Edge-Deployed Neural Net Inference
Quantized INT8 transformer architecture runs on-device on ARM Cortex-M7.
Overview
BiQadx embeds diagnostic intelligence directly into the instrument architecture. By utilizing strictly edge-native neural inference, we eliminate latency, ensure absolute patient data privacy, and deliver robust operation even in completely disconnected environments.
Architecture & Design
We train proprietary transformer models to analyze complex fluorescence decay curves and multiplexed binding kinetics. These models are distilled and quantized to run bare-metal on microcontroller endpoints. The system executes localized noise filtering, baseline correction, and non-linear curve fitting to yield deterministic quantitative targets.
Technical Specifications
Compute Architecture
ARM Cortex-M7 (600MHz)
Model Quantization
INT8 / 4-bit weights
Inference Latency
< 18ms per multi-channel read
Network Dependency
True Zero (Air-gapped capable)