Architectural Isolation as a Timing Safety Primitive for Edge AI Medical Devices: Controlled Experimental Evidence on a Shared-Silicon Platform

TL;DR

This paper demonstrates that accuracy validation and timing constraints are independent properties in edge AI medical devices, using controlled experiments on shared hardware to inform robustness verification methods.

Contribution

It provides experimental evidence of the independence of accuracy and timing constraints and proposes joint verification as a method for regulatory compliance.

Findings

CPU path latency degrades 7.2x under load, breaching clinical cycle budget

GPU path maintains latency below 11 ms under load

Both paths maintain zero Safety-Threshold Exceedance Rate (STER)

Abstract

A system can satisfy accuracy-based validation, maintain output stability (Safety-Threshold Exceedance Rate, STER, equal to zero), and still violate timing constraints under deployment load. These are structurally independent properties that current pre-market validation protocols often do not operationalize at the inference layer. This letter demonstrates their independence through a controlled same-hardware experiment: identical MobileNetV2 models are evaluated under identical adversarial load on two execution paths of the same NVIDIA Jetson Orin Nano Super, a dedicated GPU accelerator (TensorRT FP16, half-precision floating point) and a general-purpose CPU (ONNX Runtime FP32, single-precision floating point). Both paths maintain STER = 0; the CPU path (ONNX Runtime FP32) degrades 7.2x under combined load (mean latency 9.8x higher than the GPU path (TensorRT FP16), which maintains latency below 11 ms), breaching the 10 Hz clinical cycle budget by 65%. Joint STER and latency verification is proposed as a candidate method for operationalizing U.S. FDA Draft Guidance FDA-2024-D-4488 robustness requirements at the inference layer, subject to regulatory review and clinical validation.