| --- |
| model_name: AIDE-Chip-Surrogates |
| license: cc-by-nc-sa-4.0 |
| library_name: xgboost |
| pipeline_tag: tabular-regression |
| tags: |
| - computer-architecture |
| - gem5 |
| - cache |
| - surrogate-model |
| - explainable-ai |
| - shap |
| - monotonic-constraints |
| - systems-ml |
| datasets: |
| - uralstech/AIDE-Chip-15K-gem5-Sims |
| --- |
| |
| # AIDE Chip Surrogates |
|
|
| This is a collection of physics-aware, monotonicity-constrained XGBoost models that replace expensive gem5 cache simulations during design-space exploration. |
|
|
| Each model predicts either IPC or L2 miss rate for a specific workload, using only cache configuration parameters as input. The models are interpretable via SHAP and enforce microarchitectural monotonicity where physically justified. |
|
|
| This model release accompanies the paper: |
|
|
| > Udayshankar Ravikumar . Fast, Explainable Surrogate Models for gem5 Cache Design Space Exploration. Authorea. January 14, 2026. |
| > <https://doi.org/10.22541/au.176843174.46109183/v1> |
|
|
| ## Model Architecture |
|
|
| * Algorithm: XGBoost Regressor |
| * Targets: |
| * IPC |
| * L2 miss rate |
| * Features: |
| * Logâ‚‚ cache sizes & associativities |
| * Set-count proxies |
| * Cache hierarchy ratios |
| * Constraints: |
| * Monotonic constraints encoding cache physics |
| * Selective relaxation for latency-sensitive workloads |
|
|
| ## Available Models |
|
|
| | Workload | IPC Model | L2 Miss Model | |
| | ---------- | --------- | ------------- | |
| | crc32 | ✔ | ✔ | |
| | dijkstra | ✔ | ✔ | |
| | fft | ✔ | ✔ | |
| | matrix_mul | ✔ | ✔ | |
| | qsort | ✔ | ✔ | |
| | sha | ✔ | ✔ | |
| |
| Total models: **12** |
| |
| ## Performance |
| |
| * Test set accuracy: **R² ≈ 0.999** |
| * OOD validation: |
| * 26 unseen cache configurations |
| * **~817× critical-path speedup** |
| * Low absolute error even when R² is unstable |
| |
| ## Explainability |
| |
| Each model is uploaded with its SHAP summary plot. They confirm: |
| |
| * Cache sizes dominate IPC & miss behavior |
| * Associativity effects are workload-dependent |
| * Learned relationships align with microarchitectural intuition |
| |
| ## Intended Use |
| |
| * Architecture research |
| * Design-space exploration |
| * Educational use |
| * Explainable systems ML |
| |
| **Not for commercial deployment** without separate licensing. |
| |
| ## Limitations |
| |
| * Single-core, single-thread models |
| * Cache hierarchy only (no pipeline, prefetcher, or multicore effects) |
| * Accuracy depends on training coverage; extreme OOD configs are flagged |
| |
| ## Patent Notice |
| |
| The models uploaded here implement techniques described in an accompanying research paper. |
| The author has filed a pending patent application that may cover broader |
| design-space exploration workflows beyond these specific model implementations. |
| |
| The open-source license (CC BY-NC-SA 4.0) governs use of these models. |
| This notice is informational only. |
| |
| ## Pickle Model Inference |
| |
| Environment confirmed to be able to load the pickled models: |
| ``` |
| > python --version |
| Python 3.13.7 |
| > pip list |
| Package Version |
| ----------------- ----------- |
| cloudpickle 3.1.2 |
| colorama 0.4.6 |
| contourpy 1.3.3 |
| cycler 0.12.1 |
| fonttools 4.61.1 |
| joblib 1.5.3 |
| kiwisolver 1.4.9 |
| llvmlite 0.46.0 |
| matplotlib 3.10.8 |
| numba 0.63.1 |
| numpy 2.3.5 |
| packaging 25.0 |
| pandas 2.3.3 |
| pillow 12.1.0 |
| pip 25.2 |
| pyparsing 3.3.1 |
| python-dateutil 2.9.0.post0 |
| pytz 2025.2 |
| scikit-learn 1.8.0 |
| scipy 1.16.3 |
| shap 0.50.0 |
| six 1.17.0 |
| slicer 0.0.8 |
| threadpoolctl 3.6.0 |
| tqdm 4.67.1 |
| typing_extensions 4.15.0 |
| tzdata 2025.3 |
| xgboost 3.2.0 |
| ``` |
