---
language: en
license: mit
library_name: pytorch
pipeline_tag: other
tags:
  - serdes
  - lstm
  - adaptive-control
  - signal-processing
  - communications
  - equalization
  - hardware-optimization
  - real-time-systems

datasets:
  - synthetic

metrics:
  - mse
  - r2
  - mae

model-index:
  - name: adaptive-serdes-lstm-controller
    results:
      - task:
          type: regression
          name: SerDes Parameter Optimization
        dataset:
          type: synthetic
          name: SerDes Channel Characterization
        metrics:
          - type: r2_score
            value: 0.92
            name: R-squared Score
          - type: mean_absolute_error
            value: 0.05
            name: Mean Absolute Error
          - type: mse
            value: 0.003
            name: Mean Squared Error

widget:
  - text: "Channel characterization: 25.78125 Gb/s data rate with -18.22 dB insertion loss"
    example_title: "High-Speed Channel Adaptation"
---

# Adaptive SerDes LSTM Controller

## Model Description

This model implements an **Adaptive SerDes (Serializer-Deserializer) Controller** using LSTM neural networks for real-time optimization of high-speed digital communication systems. The model dynamically tunes 31 SerDes parameters to maintain optimal signal integrity across varying channel conditions.

### Key Features

- **Real-time Adaptation**: LSTM-based controller that adapts to changing channel conditions
- **Multi-Parameter Optimization**: Controls 31 SerDes parameters including FFE/DFE taps, TX swing, RX CTLE settings
- **Channel-Aware**: Integrates real S4P channel characterization data
- **High-Speed Support**: Validated up to 112 Gb/s data rates
- **Eye Diagram Optimization**: Maximizes eye height and width for optimal signal quality

### Architecture

- **Input**: 12 channel characteristics (insertion loss, group delay, return loss, etc.)
- **LSTM Layers**: 3 layers with 256 hidden units each
- **Output**: 31 SerDes control parameters
- **Total Parameters**: 1,762,079
- **Training Data**: 100,000+ channel scenarios with optimal parameter sets

## Intended Use

### Primary Use Cases

1. **Adaptive SerDes Systems**: Real-time parameter optimization in high-speed transceivers
2. **Channel Equalization**: Automatic tuning of FFE/DFE equalizers
3. **Signal Integrity Optimization**: Maintaining eye diagram quality across PVT variations
4. **Research & Development**: Baseline for adaptive communication system research

### Direct Use

```python
import torch
import numpy as np

# Load the model
model = torch.load('adaptive_serdes_lstm_controller.pth')
model.eval()

# Example channel characteristics
channel_data = torch.tensor([[
    -18.22,  # insertion_loss_db
    -16.38,  # return_loss_db
    45.2,    # group_delay_ps
    25.78125,# data_rate_gbps
    5.156,   # nyquist_freq_ghz
    0.85,    # eye_height_v
    0.65,    # eye_width_ui
    12.5,    # snr_db
    1e-12,   # ber_estimate
    0.15,    # jitter_rms_ui
    2.1,     # amplitude_v
    0.92     # quality_factor
]], dtype=torch.float32)

# Predict optimal SerDes parameters
with torch.no_grad():
    serdes_params = model(channel_data)

print(f"Optimized parameters: {serdes_params.shape}")
```

## Training Data

The model was trained on a comprehensive dataset of:

- **100,000+ channel scenarios** with varying characteristics
- **Real S4P channel measurements** from industry-standard test cases
- **Optimal parameter sets** derived from signal integrity analysis
- **Multiple data rates**: 10.3125, 25.78125, 56.0, 112.0 Gb/s

### Data Sources

- Industry-standard S4P channel characterization files
- Synthetic channel models covering extreme conditions
- Real-world backplane and cable channel measurements

## Training Procedure

### Training Hyperparameters

- **Optimizer**: Adam with weight decay (1e-5)
- **Learning Rate**: 0.001 with ReduceLROnPlateau scheduler
- **Batch Size**: 64
- **Epochs**: 500
- **Loss Function**: Mean Squared Error
- **Regularization**: Dropout (0.2), L2 regularization

### Training Results

- **Final Training Loss**: 0.0028
- **Validation Loss**: 0.0031
- **R-squared Score**: 0.92
- **Mean Absolute Error**: 0.05

## Evaluation

### Metrics

The model achieves excellent performance across multiple metrics:

| Metric                 | Value | Description                  |
| ---------------------- | ----- | ---------------------------- |
| R-squared Score               | 0.92  | Coefficient of determination |
| MAE                    | 0.05  | Mean Absolute Error          |
| MSE                    | 0.003 | Mean Squared Error           |
| Eye Height Improvement | +356% | Average eye height gain      |
| SNR Improvement        | +27%  | Signal-to-noise ratio gain   |

### Testing Data

- **Real S4P Files**: Validated on 10 industry-standard channel files
- **Data Rate Range**: 10.3125 - 112.0 Gb/s
- **Channel Types**: Backplane, cable, and connector channels
- **Loss Range**: -5 to -25 dB insertion loss

## Environmental Impact

- **Training Time**: ~2 hours on NVIDIA RTX GPU
- **Inference Time**: <1ms per prediction
- **Model Size**: 6.7 MB
- **Carbon Footprint**: Minimal due to efficient LSTM architecture

## Technical Specifications

### Model Architecture Details

```python
AdaptiveSerDesLSTM(
  (input_norm): BatchNorm1d(12)
  (lstm1): LSTM(12, 256, batch_first=True, dropout=0.2)
  (lstm2): LSTM(256, 256, batch_first=True, dropout=0.2)
  (lstm3): LSTM(256, 256, batch_first=True, dropout=0.2)
  (dropout): Dropout(p=0.2)
  (fc_layers): Sequential(
    (0): Linear(256, 128)
    (1): ReLU()
    (2): Dropout(p=0.2)
    (3): Linear(128, 64)
    (4): ReLU()
    (5): Dropout(p=0.2)
    (6): Linear(64, 31)
    (7): Tanh()
  )
  (output_norm): BatchNorm1d(31)
)
```

### Output Parameters (31 total)

**FFE Taps (7)**: Pre-cursor and post-cursor feed-forward equalizer taps
**DFE Taps (8)**: Decision feedback equalizer taps
**TX Parameters (8)**: Swing voltage, pre-emphasis, slew rate controls
**RX Parameters (8)**: CTLE settings, VGA gain, offset compensation

## Limitations

- **Channel Scope**: Optimized for electrical channels up to 112 Gb/s
- **Temperature Range**: Validated for -40°C to +85°C industrial range
- **Real-time Constraints**: Requires <1ms adaptation time for practical deployment
- **Hardware Dependencies**: Assumes standard SerDes architecture with programmable parameters

## Bias and Fairness

The model is trained on diverse channel conditions but may have biases toward:

- Common industrial channel types (backplane, cable)
- Standard data rates (10.3, 25.8, 56, 112 Gb/s)
- Specific connector and material types in training data


```

## Model Card Authors

Fidel Makatia Omusilibwa

## Model Card Contact

For questions about this model, please open an issue in the model repository or contact the author.