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Workflow
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**Pruning Workflow:**

1. **Overview:**
   - Pruning is a technique used to reduce the size of a neural network by eliminating unnecessary connections (weights).
   - The goal is to create a more efficient and lightweight model without sacrificing performance.

2. **Identifying Insignificant Weights:**
   - Train the neural network as usual.
   - Analyze the trained model to identify weights that contribute less to the overall performance.
   - Weights with low magnitudes are considered less important.

3. **Pruning Criteria:**
   - Define a pruning threshold or criteria for determining which weights to prune.
   - Common criteria include magnitude-based pruning, where weights below a certain threshold are pruned.

4. **Pruning Process:**
   - Apply the pruning criteria to the identified weights.
   - Set the weights below the threshold to zero or remove them from the network.
   - This results in a sparse or pruned model.

5. **Fine-tuning:**
   - Retrain the pruned model to recover any lost accuracy.
   - Fine-tuning helps the model adapt to the changes introduced by pruning.

6. **Benefits:**
   - Reduced model size, leading to faster inference and lower memory requirements.
   - Potential speedup during training due to the sparsity introduced by pruning.

**Quantization Workflow:**

1. **Overview:**
   - Quantization involves reducing the precision of the weights and activations in a neural network.
   - It replaces floating-point numbers with lower bit-width representations, such as integers.

2. **Quantization Levels:**
   - Choose the bit-width for quantization (e.g., 8-bit, 16-bit).
   - Lower bit-widths lead to reduced memory and computational requirements but may impact model accuracy.

3. **Quantization of Weights and Activations:**
   - Apply quantization to both the model weights and activations.
   - Convert floating-point values to their quantized equivalents using a specified quantization scheme.

4. **Quantization Schemes:**
   - Common quantization schemes include linear quantization, where values are uniformly quantized within a specified range, and non-linear quantization, which uses a non-uniform distribution.

5. **Fine-tuning:**
   - Retrain the quantized model to recover any accuracy loss.
   - Fine-tuning is crucial to adjust the model for the reduced precision.

6. **Benefits:**
   - Reduced memory footprint and storage requirements.
   - Accelerated inference due to the use of lower precision computations.
   - Improved deployment on devices with limited computational resources.



.. image:: https://github.com/satabios/sconce/blob/main/docs/source/images/sconce-workflow.jpeg?raw=true
        :align: center