# Optimizing Inference Neural networks pose unique challenges with regards to encrypted inference. Each neuron in a network applies an activation function that requires a PBS operation. The latency of a single PBS depends on the bit-width of the input of the PBS. Several approaches can be used to reduce the overall latency of a neural network. ## Circuit bit-width optimization [Quantization Aware Training](/concrete-ml/1.1/advanced-topics/quantization.md) and [pruning](/concrete-ml/1.1/advanced-topics/pruning.md) introduce specific hyper-parameters that influence the accumulator sizes. It is possible to chose quantization and pruning configurations that reduce the accumulator size. A trade-off between latency and accuracy can be obtained by varying these hyper-parameters as described in the [deep learning design guide](/concrete-ml/1.1/deep-learning/torch_support.md#configuring-quantization-parameters). ## Structured pruning While un-structured pruning is used to ensure the accumulator bit-width stays low, [structured pruning](https://pytorch.org/docs/stable/generated/torch.nn.utils.prune.ln_structured.html) can eliminate entire neurons from the network. Many neural networks are over-parametrized (since this enables easier training) and some neurons can be removed. Structured pruning, applied to a trained network as a fine-tuning step, can be applied to built-in neural networks using the [prune](/concrete-ml/1.1/developer-guide/api/concrete.ml.sklearn.base.md#method-prune) helper function as shown in [this example](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/docs/advanced_examples/FullyConnectedNeuralNetworkOnMNIST.ipynb). To apply structured pruning to custom models, it is recommended to use the [torch-pruning](https://github.com/VainF/Torch-Pruning) package. ## Rounded activations and quantizers Reducing the bit-width of the inputs to the Table Lookup (TLU) operations is a major source of improvements in the latency. Post-training, it is possible to leverage some properties of the fused activation and quantization functions expressed in the TLUs to further reduce the accumulator. This is achieved through the *rounded PBS* feature as described in the [rounded activations and quantizers reference](/concrete-ml/1.1/advanced-topics/advanced_features.md#rounded-activations-and-quantizers). Adjusting the rounding amount, relative to the initial accumulator size, can bring large improvements in latency while maintaining accuracy. ## TLU error probability adjustment Finally, the TFHE scheme exposes a TLU error probability parameter that has an impact on crypto-system parameters that influence latency. A higher probability of TLU error results in faster computations but may reduce accuracy. One can think of the error of obtaining $$T\[x]$$ as a Gaussian distribution centered on $$x$$: $$TLU\[x]$$ is obtained with probability of `1 - p_error`, while $$T\[x-1]$$, $$T\[x+1]$$ are obtained with much lower probability, etc. In Deep NNs, these type of errors can be tolerated up to some point. See the [`p_error` documentation for details](/concrete-ml/1.1/advanced-topics/advanced_features.md#approximate-computations) and more specifically the usage example of [the API for finding the best `p_error`](/concrete-ml/1.1/advanced-topics/advanced_features.md#searching-for-the-best-error-probability). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.zama.org/concrete-ml/1.1/deep-learning/optimizing_inference.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.