Add software-based CAM retrieval benchmark to compare retrieval quality
and speed against hardware simulation. Includes experiment documentation
with noise sweep analysis on CIFAR-10/100 datasets.
- Add sw_retrieval_benchmark.py for software Hamming distance Top-K retrieval
- Add test_sw_retrieval_benchmark.py with unit tests for dataset loading and metrics
- Add experiment doc (sw_hw_cam_retrieval_benchmark.md) comparing software vs hardware
- Document noise sweep impact on retrieval quality at various WRITE_NOISE_RATE values
- Rename `read_noise` scenarios and noise_mode to `read_pass_through` across
run_cam_correctness.py, test_run_cam_correctness.py, and Makefiles
- Update RTL comment in match_engine_pipeline.sv to reflect pass-through behavior
- Move unit-level cocotb tests from `tests.test_*` flat namespace to
`tests.modules.*` and `tests.top.*` subdirectory layout, matching actual
Makefile paths (hw/sim/tests/modules/..., hw/sim/tests/top/...)
- Remove redundant dual-noise subtarget from read_noise/Makefile
- Update help text and docs to reflect read-path pass-through semantics
- Add .codegraph to .gitignore
- Add just recipes for preparing CIFAR10/100 hash artifacts and running benchmarks
- Add CAM_RETRIEVAL_DATASET env var support in Makefile
- Add load_retrieval_dataset_npz() to load pre-prepared retrieval datasets
- Add label_hits counter and recall@k metric for retrieval evaluation
- Rename macro_recall to retrieval_recall to clarify semantics
- Introduce ImageHashPipeline Protocol for extensible hash computation
- Rename query_crop_index to query_index for clarity
- Make query_crop nullable to handle missing crop edge case
- Add keyword-only arguments for better API clarity
- Handle empty scene graph objects gracefully
- Add comprehensive test coverage for query_image_against_scene_graph
- Export ImageSceneGraphQueryResult and query_image_against_scene_graph from scenegraph module
- Replace inline hamming-distance-based image matching with dedicated query_image_against_scene_graph function
- Improve top_matches structure by extracting similarity scores and hash_bytes from matches
- Add .codegraph/ to gitignore (machine-local data, should not be committed)
- Add CodeGraph configuration for multi-language indexing
- Add SoftwareCamIndex class with xnor_popcount_score for CAM-style matching
- Add CamMatch and SceneGraphMatch dataclasses for query results
- Add query_by_visual_hash method to SimpleSceneGraph
- Add comprehensive tests for SoftwareCamIndex and xnor_popcount_score
- Add write_training_metrics() in new compressors/training_metrics.py
for appending epoch/step/lr/component rows as JSON Lines
- Wire --metrics-path and --log-every CLI options into train.py, passing
them to the training loop so metrics rows are written every N steps
- Accept absolute metrics paths or paths relative to output directory
- Add quantization component to loss log alongside existing distill/contrastive
- Replace inline torch.device() with get_device() utility
- Add test_hash_training_metrics.py covering multi-row JSONL append
Infrastructure:
- Pin torch 2.7.1 + CUDA 12.8 index for Linux/Windows in pyproject.toml
- Add .justfile rsync upload recipe with .stignore exclusion
- Exclude **/__marimo__ from rsync in .stignore
Dependencies updated: numpy 2.4.5, pandas 3.0.3, black 26.5.0,
click 8.4.0, contourpy, etc.
- hw/sim/tests/test_cam_perf.py: new cocotb perf test with bounded wait helpers
- scripts/sweep_cam_perf.py: sweep data model, matrix, and make command builders
- tests/test_sweep_cam_perf.py: unit tests for sweep helpers
- tests/conftest.py: pytest path configuration for scripts package
- hw/sim/Makefile: deterministic noise params override for perf test compatibility
Introduce scripts/run_cam_correctness.py — a data-driven CAM correctness
checker that runs cocotb and pytest scenarios across multiple noise modes
and CAM submodules (cam_top, cam_core_banked, match_engine_pipeline).
Outputs structured CSV results and a paper-ready Chinese summary.
Add tests/test_run_cam_correctness.py with contract tests for scenario
spec validation, command building, subprocess error handling, output file
generation, CLI defaults, and environment preservation.
