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

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

Direct Use

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
R² Score: 0.92
Mean Absolute Error: 0.05

Evaluation

Metrics

The model achieves excellent performance across multiple metrics:

Metric	Value	Description
R² 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

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

Citation

@misc{omusilibwa2024adaptive,
  title={Adaptive SerDes LSTM Controller for Real-time Signal Integrity Optimization},
  author={Fidel Makatia Omusilibwa},
  year={2024},
  howpublished={\\url{https://huggingface.co/Makatia/adaptive-serdes-lstm-controller}},
  note={LSTM-based adaptive controller for high-speed SerDes parameter optimization}
}

Model Card Authors

Fidel Makatia Omusilibwa

Model Card Contact

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