Nrflike
Nrflike is a term used in machine learning to denote a family of lightweight neural architectures optimized for real-time perception on edge devices. The core idea is to balance accuracy with low latency and memory usage by combining a compact feature extractor, a small stateful core, and a fusion mechanism that aggregates information across short temporal windows. Nrflike models are designed to operate in streaming settings, where data arrives continuously and decisions must be produced with minimal delay.
The typical architecture includes a shallow front end for feature extraction, a compact recurrent or gated
Training often uses standard supervised objectives, with optional self-supervised pretraining to improve representation learning. In deployment,
Limitations include sensitivity to hyperparameters and task-specific performance variation; there is no single canonical implementation. The