Principle:Tencent Ncnn Neural Network Inference
| Knowledge Sources | |
|---|---|
| Domains | Inference, Deep_Learning |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
Process of executing a forward pass through a neural network graph, propagating input tensors through a sequence of layers to produce output predictions.
Description
Neural network inference is the execution phase where a pre-trained model processes input data to produce predictions. Unlike training, inference only performs the forward pass (no backpropagation or weight updates). The runtime traverses the network's directed acyclic graph (DAG) from input blobs to output blobs, executing each layer's forward function in topological order.
Modern inference frameworks use a session-based pattern: an Extractor (or session) is created from the loaded network, inputs are bound to named input blobs, and outputs are retrieved from named output blobs. This pattern enables lazy evaluation — only the subgraph required to compute the requested output blobs is executed.
Key optimizations in inference runtimes include intermediate blob recycling (light mode), SIMD-packed element processing, and on-demand layer execution (only computing paths needed for requested outputs).
Usage
Use this principle after model loading and input preprocessing. It is the core execution step in every inference pipeline. The same loaded network can create multiple independent Extractors for concurrent inference on different inputs.
Theoretical Basis
Inference follows a topological execution over the network DAG:
Pseudo-code:
// Abstract inference algorithm
extractor = net.create_session()
extractor.set_input("input_blob", preprocessed_tensor)
// Lazy evaluation: only compute layers needed for output
result = extractor.get_output("output_blob")
// Internally: topological sort -> execute layers -> return output tensor
Light mode optimization: When enabled (default), intermediate blob data is freed as soon as all downstream consumers have read it, minimizing peak memory usage during inference.
Lazy evaluation: The runtime traces backward from the requested output blob to determine which layers need execution, skipping unused branches of the graph.