Implementation and Optimization of Deep Learning Models for Multimodal Ischemic Stroke Segmentation on Embedded Systems Using Quantization Techniques

Deploying deep learning segmentation models on embedded hardware requires reducing their numerical precision through quantization, an optimization whose aim is not to improve segmentation quality but to preserve it while reducing latency and energy consumption. Its effect, however, differs between architectures. This work studies whether quantization behaves the same in a convolutional network as in a Transformer, and how the compiler resolves the requested precision on two Jetson hardware–software configurations. Two ischemic-stroke segmentation models trained on the ISLES’24 dataset, M1 (convolutional nnU-Net, ∼30 M parameters, InstanceNorm) and M3 (SegFormer3D, a 4.5 M-parameter Transformer with 32 LayerNorm layers), are deployed on a Jetson Xavier NX (TensorRT 8.5) and a Jetson Orin Nano (TensorRT 10.3). Each model is compiled with TensorRT in FP32, FP16 and INT8 calibrated through the implicit (calibrator-based) workflow, with an additional mixed-precision variant for M3, and is evaluated on the same 15 test patients with the official ISLES’24 metrics (DSC, AVD, lesion-wise F1 and ALCD), latency, power, estimated energy per volume and memory, using paired per-patient statistics. FP16 preserves the quality of both models and provides most of the efficiency gain. In M1, 44% of the layers run in genuine INT8, and INT8 causes a small, statistically inconclusive DSC loss (0.169→0.154 on the Orin) but clearly more fragmented predictions (ALCD +6.9 lesions per patient), with almost no additional latency gain on the Orin. In M3, the calibrated INT8 engine keeps 97.7% of its layers in FP32, and the mixed-precision engine, with the LayerNorm layers forced to FP16, is turned by the compiler into a de facto FP16 engine (89.5% of its layers); within the tested implementation, these observations are consistent with the activation outliers that the literature associates with LayerNorm, but they do not establish it as the cause. The FP16 engine of M3 reproduces the reference (DSC 0.163) to the third decimal, and the FP32 engines of M1 match the original PyTorch model under the same pipeline, which validates the integrity of both conversions. Requesting INT8 does not guarantee INT8 execution: the effective precision is set by the compiler and depends on the model architecture. These results characterise the computational feasibility of the deployment, not the clinical readiness of the models.