Knowledge Distillation

A compression technique that reduces the numerical precision of a neural network's weights and activations—typically from 32-bit floating point (FP32) to 8-bit integers (INT8)—shrinking memory footprint by up to 4x. Post-training quantization uses a small calibration dataset to minimize accuracy loss without retraining, while quantization-aware training simulates low-precision effects during fine-tuning for superior results. Critical for deploying diagnostic models on embedded medical devices with limited RAM.

A compression method that removes entire structural components—such as convolutional channels, attention heads, or entire layers—from a neural network. Unlike unstructured pruning, which zeroes out individual weights and creates sparse matrices, structured pruning produces a smaller, dense model that is immediately accelerated by standard hardware without specialized sparse computation libraries. This makes it ideal for edge deployment where custom inference engines may not be available.

A model optimization paradigm that incorporates the specific constraints of a target hardware accelerator—such as latency budgets, memory bandwidth, and power limits—directly into the neural network training process. By using differentiable proxies for hardware metrics, the optimizer searches for architectures that balance accuracy with real-world deployment feasibility. This approach often yields superior results compared to post-hoc compression, as the model is designed from the ground up for its deployment target.

An NVIDIA SDK for high-performance deep learning inference that includes a model optimizer and a runtime engine. TensorRT performs layer fusion, kernel auto-tuning, and precision calibration to deliver low-latency, high-throughput inference on NVIDIA GPUs and Jetson edge devices. It supports importing models from all major frameworks and can apply INT8 and FP16 optimizations. Widely used as the inference backend for scanner-side AI deployments in medical imaging.

A cross-platform, open-source inference engine designed to accelerate models in the Open Neural Network Exchange (ONNX) format. It provides a unified runtime across diverse hardware configurations—from cloud GPUs to edge CPUs and NPUs—through a pluggable execution provider architecture. Supports graph optimizations, quantization, and hardware-specific acceleration. Essential for maintaining framework-agnostic deployment pipelines in heterogeneous clinical device fleets.

A set of statistical methods that equip a diagnostic AI model with the ability to estimate the confidence of its own predictions. Techniques include Monte Carlo Dropout, deep ensembles, and conformal prediction. When a distilled student model is deployed at the edge, uncertainty quantification becomes critical—it enables the system to flag ambiguous cases for human review, preventing silent failures on out-of-distribution anatomy or pathology not seen during training.