Deterministic Edge Computing for Bedside SaMD & Critical Biosignal Inference

SaMDCompute executes complex physiological inference directly at the point of care, eliminating cloud dependency and latency jitter for critical arrhythmia and respiratory decisions.

SaMDCompute Bedside Edge Architecture

Deterministic Processing Enclave and Biosignal Accelerator

Central System: Clinical Edge Inference Engine - Deterministic RTOS Core

Clinical IoMT Nodes

Quantized Neural Engine
RTOS Memory Enclave
Bedside Telemetry Ingress
Hazard Supervisor
EHR Export Bridge

Architectural Layers & Regulatory Standards

Deterministic Microkernel Layer (seL4 Microkernel / FreeRTOS MPU Medical Profile): Formally verified microkernel enforcing priority preemption and bounded interrupt latency.
Memory Protection & Isolation Enclave (ARM TrustZone-M / RISC-V PMP (Physical Memory Protection)): Hardware-enforced MPU boundaries preventing non-critical logging tasks from corrupting vital algorithms.
Hardware-Accelerated Neural Inference (CMSIS-NN Vector Kernels / INT8 Quantized Tensor Arrays): Low-power vector NPU executing quantized 1D/2D CNNs for real-time electrophysiological wave analysis.
Clinical Decision & Dispatch Interface (IEC 60601-1-8 Audio/Visual Hazard Controller): Synchronous dispatch of high-priority alarms to bedside displays and central nurse consoles.

Key Metrics: < 100 µs (Worst-Case Interrupt Latency) | 100% (Formal Memory Safety Guarantee) | INT8 (Quantized Waveform Inference) | Class C (IEC 62304 Software Architecture)

Predictable Low-Latency Computing Where Seconds Save Lives

Cloud-based medical algorithms are vulnerable to internet outages and packet jitter. SaMDCompute standardizes edge acceleration platforms that guarantee worst-case execution times for real-time hemodynamic alerting and automated drug delivery.

Core Engineering Areas

RTOS in Class C Medical Devices — Hard real-time deterministic scheduling algorithms preventing priority inversion in ventilators and monitors.
Hardware-Enforced Memory Isolation — Isolating untrusted third-party drivers from core patient safety algorithms.
Deterministic Deep Learning Inference at Bedside — Achieving predictable latency and zero memory fragmentation in medical AI inference.

Technical Articles

Related Cluster Architecture Specifications

HealthVigilance — Zero-Trust IoMT Architecture, SaMD Risk Engineering & Autonomous Clinical Post-Market Vigilance
AgenticSaMD — Autonomous Agentic Software as a Medical Device, Multi-Agent Clinical Decision Support & FDA GMLP Guardrails
AITelemonitoring — Autonomous AI Telemonitoring, Virtual Nursing Assistants & Predictive Clinical Deterioration Scoring
BCI2IoMT — Brain-Computer Interface IoMT Gateways, Neural Decoding & Electrophysiological Streaming