Update README.md

README.md

@@ -1,240 +1,227 @@
----
-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
-        metrics:
-          - type: r2_score
-            value: 0.92
-            name: R² 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
-- **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
-
-```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
-
-## Citation
-
-```bibtex
-@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
-
-For questions about this model, please open an issue in the model repository or contact the author.
-
----
-
-_This model card was generated following the Model Card Framework for ML model documentation._
+---
+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
+        metrics:
+          - type: r2_score
+            value: 0.92
+            name: R² 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
+- **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
+
+```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.